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Tytuł:
Selected problem of structure optimization for Artificial Neural Networks with forward connections
Autorzy:
Płaczek, S.
Powiązania:
https://bibliotekanauki.pl/articles/376117.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
artificial neural network
network structure
structure optimization
Opis:
The problem of Artificial Neural Network (ANN) structure optimization related to the definition of optimal number of hidden layers and distribution of neurons between layers depending on selected optimization criterion and inflicted constrains. The article presents the resolution of the optimization problem. The function describing the number of subspaces is given, and the minimum number of layers as well as the distribution of neurons between layers shall be found.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2014, 80; 191-197
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Polish emotional speech recognition using artifical neural network
Autorzy:
Powroźnik, P.
Powiązania:
https://bibliotekanauki.pl/articles/102146.pdf
Data publikacji:
2014
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
emotional speech
artificial neural network
communication
Opis:
The article presents the issue of emotion recognition based on polish emotional speech analysis. The Polish database of emotional speech, prepared and shared by the Medical Electronics Division of the Lodz University of Technology, has been used for research. The following parameters extracted from sampled and normalised speech signal has been used for the analysis: energy of signal, speaker’s sex, average value of speech signal and both the minimum and maximum sample value for a given signal. As an emotional state a classifier fof our layers of artificial neural network has been used. The achieved results reach 50% of accuracy. Conducted researches focused on six emotional states: a neutral state, sadness, joy, anger, fear and boredom.
Źródło:
Advances in Science and Technology. Research Journal; 2014, 8, 24; 24-27
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Influence of the Artificial Neural Network type on the quality of learning on the Day-Ahead Market model at Polish Power Exchange joint-stock company
Autorzy:
Ruciński, Dariusz
Powiązania:
https://bibliotekanauki.pl/articles/1819257.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
Perceptron Artificial Neural Network
Radial Artificial Neural Network
Recursive Artificial Neural Network
neural model quality
Day-Ahead Market
Polish Power Exchange
Mean square error
determination index
Opis:
The work contains the results of the Day-Ahead Market modeling research at Polish Power Exchange taking into account the numerical data on the supplied and sold electricity in selected time intervals from the entire period of its operation (from July 2002 to June 2019). Market modeling was carried out based on three Artificial Neural Network models, ie: Perceptron Artificial Neural Network, Recursive Artificial Neural Network, and Radial Artificial Neural Network. The examined period of the Day-Ahead Market operation on the Polish Power Exchange was divided into sub-periods of various lengths, from one month, a quarter, a half a year to the entire period of the market's operation. As a result of neural modeling, 1,191 models of the Market system were obtained, which were assessed according to the criterion of the least error MSE and the determination index R2.
Źródło:
Studia Informatica : systems and information technology; 2019, 1-2(23); 77--93
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of an artificial neural network for planning the trajectory of a mobile robot
Autorzy:
Białek, Marcin
Nowak, Patryk
Rybarczyk, Dominik
Powiązania:
https://bibliotekanauki.pl/articles/384525.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
artificial neural network
mobile robot
machine vision
Opis:
This paper presents application of a neural network in the task of planning a mobile robot trajectory. First part contains a review of literature focused on the mobile robots’ orientation and overview of artificial neural networks’ application in area of robotics. In these sections devices and approaches for collecting data of mobile robots environment have been specified. In addition, the principle of operation and use of artificial neural networks in trajectory planning tasks was also presented. The second part focuses on the mobile robot that was designed in a 3D environment and printed with PLA material. The main onboard logical unit is Arduino Mega. Control system consist of 8-bits microcontrollers and 13 Mpix camera. Discussion in part three describes the system positioning capability using data from the accelerometer and magnetometer with overview of data filtration and the study of the artificial neural network implementation to recognize given trajectories. The last chapter contains a summary with conclusions.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 13-23
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Prediction Study on Bremsstrahlung Photon Flux of Tungsten as a Radiological Anode Material by using MCNPX and ANN Modeling
Autorzy:
Tekin, H.
Kara, U.
Manici, T.
Altunsoy, E.
Erguzel, T.
Powiązania:
https://bibliotekanauki.pl/articles/1030108.pdf
Data publikacji:
2017-09
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
artificial neural network
Monte Carlo
medical imaging
Opis:
Medical imaging is a technique that is mostly known as visual representations of the parts of body for clinical scans and analysis. In imaging process for medical purpose there take part radiologists, radiographers/radiology technicians, medical physicists, sonographers, nurses, and engineers. As an apart issue from the medical imaging devices, we can treat X-rays using devices such as radiography, computed tomography, fluoroscopy, dental cone-beam computed tomography, and mammography. All these devices are to perform X-ray using during medical imaging process. An X-ray beam is generated in a vacuum tube that is principally composed of an anode and a cathode material to produce X-ray beams, whose name is X-ray tube. The anode represents the component in which the X-ray beam produced that made from a piece of metal. For decades, tungsten (W) has been used as an anode material of various X-ray tubes. Tungsten has high atomic number and high melting point of 3370°C with low rate of volatilization. In this study, we performed Monte Carlo simulation for flux calculations of W target by using MCNP-X general purpose code and considered result as a data set for artificial neural network. It can be concluded that the results agreed well between Monte Carlo simulation and artificial neural network prediction.
Źródło:
Acta Physica Polonica A; 2017, 132, 3; 433-435
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Monitoring of the average cutting forces from controller signals using artificial neural networks
Autorzy:
Bugdayci, Nevzat Bircan
Wegener, Konrad
Postel, Martin
Powiązania:
https://bibliotekanauki.pl/articles/2171771.pdf
Data publikacji:
2022
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
milling
cutting force monitoring
artificial neural network
Opis:
A new approach is presented to monitor the average cutting forces that are used for the calculation of the average cutting coefficients through neural networks using available controller signals. The cutting forces and the relevant controller signals are measured using a dynamometer and commercially available software supplied by the controller manufacturer in the calibration stage. Then a neural network is trained, which treats these controller signals as inputs and the cutting forces as the outputs. Finally, the average cutting forces for a new milling operation are predicted using the trained neural network without using a dynamometer. The proposed approach is validated using an experimental study, where a good match between predictions and measured forces is achieved. It is also shown that cutting coefficients can be calibrated and stability lobe diagrams can be generated using this method.
Źródło:
Journal of Machine Engineering; 2022, 22, 4; 54--70
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Preface to special issue on Modern Intelligent Systems Concepts II
Autorzy:
Idrissi, Abdellah
Powiązania:
https://bibliotekanauki.pl/articles/2141893.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
modern intelligent systems
artificial neural network
ANN
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 35-36
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Artificial Neural Network into the Water Level Modeling and Forecast
Autorzy:
Sztobryn, M.
Powiązania:
https://bibliotekanauki.pl/articles/116204.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
artificial neural network (ANN)
hydrography
coastal area
Opis:
The dangerous sea and river water level increase does not only destroy the human lives, but also generate the severe flooding in coastal areas. The rapidly changes in the direction and velocity of wind and associated with them sea level changes could be the severe threat for navigation, especially on the fairways of small fishery harbors located in the river mouth. There is the area of activity of two external forcing: storm surges and flood wave. The aim of the work was the description of an application of Artificial Neural Network (ANN) methodology into the water level forecast in the case study field in Swibno harbor located is located at 938.7 km of the Wisla River and at a distance of about 3 km up the mouth (Gulf of Gdansk ‐ Baltic Sea).
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2013, 7, 2; 219-223
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Gap Filling of Daily Sea Levels by Artificial Neural Networks
Autorzy:
Pashova, L.
Koprinkova-Hristova, P. D.
Popova, S.
Powiązania:
https://bibliotekanauki.pl/articles/116147.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
artificial neural network (ANN)
hydrography
Black Sea
Opis:
In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN) architectures ‐ Feed‐ Forward Backpropagation (FFBP) and recurrent Echo state network (ESN). In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5‐years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real‐time interpolation of missing data in the time series.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2013, 7, 2; 225-232
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hyperparameter optimization of artificial neural networks to improve the positional accuracy of industrial robots
Autorzy:
Uhlmann, Eckart
Polte, Mitchel
Blumberg, Julian
Li, Zhoulong
Kraft, Adrian
Powiązania:
https://bibliotekanauki.pl/articles/1429023.pdf
Data publikacji:
2021
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
artificial neural network
robot calibration
hyperparameter optimization
Opis:
Due to the rising demand for individualized product specifications and short product innovation cycles, industrial robots gain increasing attention for machining operations as milling and forming. Limitations in their absolute positional accuracy are addressed by enhanced modelling and calibration techniques. However, the resulting absolute positional accuracy stays in a range still not feasible for general purpose milling and forming tolerances. Improvements of the model accuracy demand complex, often not accessible system knowledge on the expense of realtime capability. This article presents a new approach using artificial neural networks to enhance positional accuracy of industrial robots. A hyperparameter optimization is applied, to overcome the downside of choosing an appropriate artificial neural network structure and training strategy in a trial and error procedure. The effectiveness of the method is validated with a heavy-duty industrial robot. It is demonstrated that artificial neural networks with suitable hyperparameters outperform a kinematic model with calibrated geometric parameters.
Źródło:
Journal of Machine Engineering; 2021, 21, 2; 47-59
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Spectral methods in Polish emotional speech recognition
Autorzy:
Powroźnik, P.
Czerwiński, D.
Powiązania:
https://bibliotekanauki.pl/articles/102087.pdf
Data publikacji:
2016
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
artificial neural network
spectrogram
emotional speech recognition
Opis:
In this article the issue of emotion recognition based on Polish emotional speech signal analysis was presented. The Polish database of emotional speech, prepared and shared by the Medical Electronics Division of the Lodz University of Technology, has been used for research. Speech signal has been processed by Artificial Neural Networks (ANN). The inputs for ANN were information obtained from signal spectrogram. Researches were conducted for three different spectrogram divisions. The ANN consists of four layers but the number of neurons in each layer depends of spectrogram division. Conducted researches focused on six emotional states: a neutral state, sadness, joy, anger, fear and boredom. The averange effectiveness of emotions recognition was about 80%.
Źródło:
Advances in Science and Technology. Research Journal; 2016, 10, 32; 73-81
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of smart sorting machine using artificial intelligence for chili fertigation industries
Autorzy:
Abdul Aziz, M. F.
Bukhari, W. M.
Sukhaimie, M. N.
Izzuddin, T.A.
Norasikin, M.A.
Rasid, A. F. A.
Bazilah, N. F.
Powiązania:
https://bibliotekanauki.pl/articles/2141810.pdf
Data publikacji:
2021
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
precision agriculture
artificial neural network
smart fertigation
Opis:
This paper presents an automation process is a need in the agricultural industry specifically chili crops, that implemented image processing techniques and classification of chili crops usually based on their color, shape, and texture. The goal of this study was to develop a portable sorting machine that will be able to segregate chili based on their color by using Artificial Neural Network (ANN) and to analyze the performance by using the Plot Confusion method. A sample of ten green chili images and ten red chili images was trained by using Learning Algorithm in MATLAB program that included a feature extraction process and tested by comparing the performance with a larger dataset, which are 40 samples of chili images. The trained network from 20 samples produced an overall accuracy of 80 percent and above, while the trained network from 40 samples produced an overall accuracy of 85 percent. These results indicate the importance of further study as the design of the smart sorting machine was general enough to be used in the agricultural industry that requires a high volume of chili crops and with other differentiating features to be processed at the same time. Improvements can be made to the sorting system but will come at a higher price.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 4; 44-52
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research and applications of artificial neural networks in spatial analysis: Review
Autorzy:
Garczyńska, Ilona
Powiązania:
https://bibliotekanauki.pl/articles/29521035.pdf
Data publikacji:
2023
Wydawca:
Akademia Morska w Szczecinie. Wydawnictwo AMSz
Tematy:
spatial analysis
GIS
artificial neural network
artificial intelligence
geosciences
Opis:
The conducted review presents the possibility of using artificial neural networks in sectors related to environmental protection, agriculture, forestry, land uses, groundwater and bathymetric. Today there is a lot of research in these areas with different research methodologies. The result is the improvement of decision-making processes, design, and prediction of certain events that, with appropriate intervention, can prevent severe consequences for society. The review shows the capabilities to optimize and automate the processes of modeling urban and land dynamics. It examines the forecasts of assessment of the damage caused by natural phenomena. Detection of environmental changes via the analysis of certain time intervals and classification of objects on the basis of different images is presented. The practical aspects of this work include the ability to choose the correct artificial neural network model depending on the complexity of the problem. This factor is a novel element since previously reviewed articles did not encounter a study of the correlation between the chosen model or algorithm, depending on the use case or area of the problem. This article seeks to outline the reason for the interest in artificial intelligence. Its purpose is to find answers to the following questions: How can artificial neural networks be used for spatial analysis? What does the implementation of detailed algorithms depend on? It is proved that an artificial intelligence approach can be an effective and powerful tool in various domains where spatial aspects are important.
Źródło:
Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2023, 74 (146); 35-45
1733-8670
2392-0378
Pojawia się w:
Zeszyty Naukowe Akademii Morskiej w Szczecinie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Effect of Nanomaterial Type on Water Disinfection Using Data Mining
Autorzy:
Hamdan, Mohammad
Khalil, Rana Haj
Abdelhafez, Eman
Ajib, Salman
Powiązania:
https://bibliotekanauki.pl/articles/24201710.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
water disinfection
artificial neural network
nanotechnology
data mining
Opis:
Multiple linear regression and artificial neural network (ANN) models were utilized in this study to assess the type influence of nanomaterials on polluted water disinfection. This was accomplished by estimating E. coli (E.C) and the total coliform (TC) concentrations in contaminated water while nanoparticles were added at various concentrations as input variables, together with water temperature, PH, and turbidity. To achieve this objective, two approaches were implemented: data mining with two types of artificial neural networks (MLP and RBF), and multiple linear regression models (MLR). The simulation was conducted using SPSS software. Data mining was revealed after the estimated findings were checked against the measured data. It was found that MLP was the most promising model in the prediction of the TC and E.C concentration, s followed by the RBF and MLR models, respectively.
Źródło:
Journal of Ecological Engineering; 2023, 24, 4; 244--251
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of artificial neural networks to predict the deflections of reinforced concrete beams
Autorzy:
Kaczmarek, M.
Szymańska, A.
Powiązania:
https://bibliotekanauki.pl/articles/178826.pdf
Data publikacji:
2016
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
reinforced concrete beams
research
deflection
artificial neural network
Opis:
Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.
Źródło:
Studia Geotechnica et Mechanica; 2016, 38, 2; 37-46
0137-6365
2083-831X
Pojawia się w:
Studia Geotechnica et Mechanica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evidence Based Diagnosis of Mesothelioma
Autorzy:
Malhotra, Isha
Tayal, Akash
Powiązania:
https://bibliotekanauki.pl/articles/1159560.pdf
Data publikacji:
2018
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Artificial neural Network
Asbestos
Mesothelioma
Multilayer Perceptron
epochs
Opis:
The aim of this study is to extract the hidden patterns by using data mining and artificial intelligence techniques. The concept of artificial neural network depends on the idea that we can imitate the working of human brain by making the right links. Artificial Intelligence has always helped in many research areas including medical diagnosis. One of the basic methodologies for training and testing a network by utilizing medical information is discussed here. We have used SAS for analyzing our data and applying feed forward and back propagation mechanism for our diagnosis. The feed forward neural network with back propagation algorithm can be used to identify the diseased ones among different set of admitted individuals. In this paper, we have used multi-layer neural network to achieve the best performance with the minimum epoch (training iterations) and training time.
Źródło:
World Scientific News; 2018, 113; 117-129
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Interpolation of soil infiltration in furrow irrigation: Comparison of kriging, inverse distance weighting, multilayer perceptron and principal component analysis methods
Autorzy:
Alipour, N.
Nasseri, A.
Mohammbdi, T.A.
Pazira, E.
Powiązania:
https://bibliotekanauki.pl/articles/971544.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:
artificial neural network
geostatistical analysis
irrigation
soil infiltration
Opis:
Study on soil infiltration rate as part of water cycle is essential for managing water resources and designing irrigation systems. The present study was conducted with the aim to compare Kriging, inverse distance weighting (IDW), multilayer perceptron (MLP) and principal component analysis (PCA) methods in the interpolation of soil infiltration in furrow irrigation, and determine the best interpolation method. To conduct infiltration tests, furrows were made on the farm in four triad groups. Infiltration through the blocked furrows method was measured 10, 20, 30, 40, 50, 60, 90, 120, 150, 160, 180 and 210 min after irrigation at a 10-meter distance in each furrow. Data were analyzed by GS+ and Neuro Solutions (NS) software packages. In this study, the maximum error (ME), mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative error (RE) and correlation coefficient (r) were used to compare the interpolation methods. The results of analysis of variance (ANOVA) indicated that differences in methods based on RMSE, MBE, MAE and ME indices were not significant; however, this difference was significant based on r and RE indices. According to the ANOVA results, it can be said that the PCA method with a r of 0.69 and RE of 31%, was predicted with a higher accuracy as compared to other methods.
Źródło:
Polish Journal of Soil Science; 2019, 52, 1; 59-74
0079-2985
Pojawia się w:
Polish Journal of Soil Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks
Autorzy:
Turp, S. M.
Powiązania:
https://bibliotekanauki.pl/articles/204724.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
wastewater
treatment efficiency
adsorption
perlite
artificial neural network
Opis:
This study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.
Źródło:
Archives of Environmental Protection; 2017, 43, 4; 26-32
2083-4772
2083-4810
Pojawia się w:
Archives of Environmental Protection
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of artificial neural networks in preliminary selection of pigmented lesions for further melanoma diagnosis
Autorzy:
Kisielińska-Ptasznik, Anna
Figielska, Ewa
Powiązania:
https://bibliotekanauki.pl/articles/1397478.pdf
Data publikacji:
2020
Wydawca:
Warszawska Wyższa Szkoła Informatyki
Tematy:
artificial neural network
data pre-processing
melanoma diagnosis
Opis:
The paper deals with the problem of preliminary selection of pigmented lesionsfor further melanoma diagnosis. Several algorithms for input data pre-processingare proposed and artificial neural network for the examination of pigmented lesions is used. Computational results are reported.
Źródło:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2020, 14, 22; 23-38
1896-396X
2082-8349
Pojawia się w:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Decomposition and the principle of interaction prediction in hierarchical structure of learning algorithm of ANN
Autorzy:
Płaczek, S.
Powiązania:
https://bibliotekanauki.pl/articles/376418.pdf
Data publikacji:
2015
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
artificial neural network
hierarchy
decomposition
coordination
coordination principle
Opis:
For the most popular ANN structure with one hidden layer, decomposition is done into two sub-networks. These sub-networks form the first level of the hierarchical structure. On the second level, the coordinator is working with its own target function. In the hierarchical systems theory three coordination strategies are defined. For the ANN learning algorithm the most appropriate is the coordination by the principle of interaction prediction. Implementing an off-line algorithm in all sub-networks makes the process of weight coefficient modification more stable. In the article, the quality and quantity characteristics of a coordination algorithm and the result of the learning algorithm for all sub-networks are shown. Consequently, the primary ANN achieves the global minimum during the learning process.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2015, 84; 113-120
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wpływ opisu danych na efektywność uczenia oraz pracy sztucznej sieci neuronowej na przykładzie identyfikacji białek
Influence of data description on efficiency of learning and job artificial neural network on example of identification of proteins
Autorzy:
BARTMAN, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/457310.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Rzeszowski
Tematy:
sztuczna sieć neuronowa
uczenie
artificial neural network
learning
Opis:
Uczenie jednokierunkowych wielowarstwowych sztucznych sieci neuronowych jest zagadnieniem szeroko omawianym w literaturze. Autorzy większości opracowań skupiają się na metodach uczenia, zdecydowanie mniej prac poświęconych jest wpływowi preprocesingu danych na uczenie i efektywność pracy sieci. Skoro uczenie sztucznych sieci neuronowych jest szukaniem funkcji odwzorowującej zbiór danych wejściowych w zbiór oczekiwanych odpowiedzi, to czego możemy oczekiwać, jeżeli zmienimy opis danych uczących? Zmienia się funkcja odwzorowująca, a więc szukamy innej funkcji, zatem jest możliwe, iż sposób kodowania danych wpływa na efektywność uczenia i pracy sieci. Niniejsza praca dotyka przedstawione zagadnienie badając wpływ sposobu zakodowania opisu białek na efektywność uczenia oraz pracy sieci neuronowej identyfikującej rodzaj białka
Learning feedforward multilayer neural networks is an issue widely discussed in the literature. The authors of the most works focus on methods of learning, much less work is devoted to the influence of data preprocessing on learning and the efficiency of the network. If learning of artificial neural networks is finding the mapping function set of input data into a set of expected responses, what you can expect if you change the description of the data learners? Changes of mapping functions, and so we are looking for another function, so it is possible that the encoding of data affects the efficiency of learning and job of the network. This paper touches the issue presented by examining the impact of coding method information about the proteins on the effectiveness of learning and the work of the neural network identifies the type of protein.
Źródło:
Edukacja-Technika-Informatyka; 2013, 4, 2; 358-365
2080-9069
Pojawia się w:
Edukacja-Technika-Informatyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving estimation accuracy of metallurgical performance of industrial flotation process by using hybrid genetic algorithm – artificial neural network (GA-ANN)
Autorzy:
Allahkarami, E.
Salmani Nuri, O.
Abdollahzadeh, A.
Rezai, B.
Maghsoudi, B.
Powiązania:
https://bibliotekanauki.pl/articles/109424.pdf
Data publikacji:
2017
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
artificial neural network
genetic algorithm
prediction
copper flotation
Opis:
In this study, a back propagation feed forward neural network, with two hidden layers (10:10:10:4), was applied to predict Cu grade and recovery in industrial flotation plant based on pH, chemical reagents dosage, size percentage of feed passing 75 μm, moisture content in feed, solid ratio, and grade of copper, molybdenum, and iron in feed. Modeling is performed basing on 92 data sets under different operating conditions. A back propagation training was carried out with initial weights randomly mode that may lead to trapping artificial neural network (ANN) into the local minima and converging slowly. So, the genetic algorithm (GA) is combined with ANN for improving the performance of the ANN by optimizing the initial weights of ANN. The results reveal that the GA-ANN model outperforms ANN model for predicting of the metallurgical performance. The hybrid GA-ANN based prediction method, as used in this paper, can be further employed as a reliable and accurate method, in the metallurgical performance prediction.
Źródło:
Physicochemical Problems of Mineral Processing; 2017, 53, 1; 366-378
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Introducing artificial neural network in ontologies alignment process
Autorzy:
Djeddi, W. E.
Khadir, M. T.
Powiązania:
https://bibliotekanauki.pl/articles/206314.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
artificial neural network
training
ontology alignment
WordNet
XMap++
Opis:
Ontology alignment uses different similaritymeasures of different categories such as string, linguistic, and structural based similarity measures to understand ontologies’ semantics. A weights vector must, therefore, be assigned to these similarity measures, if a more accurate and meaningful alignment result is favored. Combining multiple measures into a single similarity metric has been traditionally solved using weights determined manually by an expert, Or calculated through general methods (e.g. average or sigmoid function) that do not provide optimal results. In this paper, we propose an artificial neural network algorithm to ascertain how to Combie multiple similarity measures into a single aggregated metric with the final aim of improving the ontology alignment quality. XMap++ is applied to benchmark tests at OAEI campaign 2010. Results show that neural network boosts the performance in most cases, and that the proposed novel approach is competitive with top-ranked system.
Źródło:
Control and Cybernetics; 2012, 41, 4; 743-759
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Artificial Neural Network: A Case of Single Point Incremental Forming (SPIF) of Cu67Zn33 Alloy
Autorzy:
Oraon, Manish
Sharma, Vinay
Powiązania:
https://bibliotekanauki.pl/articles/1841429.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
SPIF
input variable
artificial neural network
surface roughness
Opis:
Artificial neural network (ANN), a Computational tool that is frequently applied in the modeling and simulation of manufacturing processes. The emerging forming technique of sheet metal which is typically called single point incremental forming (SPIF) comes into the map and the research interest towards its technological parameters. The surface quality of the end product is a major issue in SPIF, which is more critical with the hard metals. The part of the brass metal is demanded in many industrial uses because of its high load-carrying capacity and its wear resistance property. Considering the industrial interest and demand of the brass metal products, the present study is done with the SPIF experiment on calamine brass Cu67Zn33 followed by an ANN analysis for predicting the absolute surface roughness. The modeling result shows a close agreement with the measured data. The minimum and maximum errors are found in experiment 3 and experiment 7 respectively. The error of predicted roughness is found in the range of –30.87 to 20.23 and the overall coefficient of performance of ANN modeling is 0.947 which is quite acceptable.
Źródło:
Management and Production Engineering Review; 2021, 12, 1; 17-23
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Designing a Compact Microstrip Antenna Using the Machine Learning Approach
Autorzy:
Sharma, Kanhaiya
Pandey, Ganga Prasad
Powiązania:
https://bibliotekanauki.pl/articles/1839319.pdf
Data publikacji:
2020
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
artificial neural network
dual band
microstrip antenna
notch
Opis:
This paper presents how machine learning techniques may be applied in the process of designing a compact dual-band H-shaped rectangular microstrip antenna (RMSA) operating in 0.75–2.20 GHz and 3.0–3.44 GHz frequency ranges. In the design process, the same dimensions of upper and lower notches are incorporated, with the centered position right in the middle. Notch length and width are verified for investigating the antenna. An artificial neural network (ANN) model is developed from the simulated dataset, and is used for shape prediction. The same dataset is used to create a mathematical model as well. The predicted outcome is compared and it is determined that the model relying on ANN offers better results.
Źródło:
Journal of Telecommunications and Information Technology; 2020, 4; 44-52
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sewage Volume Forecasting on a Day-Ahead Basis – Analysis of Input Variables Uncertainty
Autorzy:
Jurasz, Jakub
Piasecki, Adam
Kaźmierczak, Bartosz
Powiązania:
https://bibliotekanauki.pl/articles/125162.pdf
Data publikacji:
2019
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
artificial neural network
error forecasting
exogenous variable uncertainty
Opis:
Water consumption and the resulting sewage volume (both strongly impacted by meteorological parameters) are of key importance for an efficient and sustainable operation of waterworks and wastewater treatment plants. Therefore, the objective of this research is to analyze the potential impact of input variables uncertainty on the performance of sewage volume forecasting model. The research is based on a real, three-year long, daily time series collected from Toruń (Poland). The used time series encompassed: sewage volume, water consumption, rainfall, temperature, precipitation, evaporation, sunshine duration and precipitation at a six hours interval. Neural network has been selected as a forecasting tool a multi-layer perceptron artificial. , a simulation model for the sewage volume was created which considered the above-mentioned time series as exogenous variables. Further, its performance was tested assuming that all non-historical input variables are prone to their individual forecasting errors. The analysis was dedicated firstly to each variable individually and later the potential of all of them being uncertain was tested. A lack of correlation between the input variables error was assumed. The research provides an interesting solution for visualizing the quality and actual performance of forecasting models where some or all of input variables has to be forecast.
Źródło:
Journal of Ecological Engineering; 2019, 20, 9; 70-79
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning in SMED
Autorzy:
Kutschenreiter-Praszkiewicz, I.
Powiązania:
https://bibliotekanauki.pl/articles/99646.pdf
Data publikacji:
2018
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
time standard
human activity
SMED
artificial neural network
Opis:
The paper discusses Single Minute Exchange of Die (SMED) and machine learning methods, such as neural networks and a decision tree. SMED is one of lean production methods for reducing waste in the manufacturing process, which helps to reorganize a conversion of the manufacturing process from current to the next product. SMED needs set-up activity analyses, which include activity classification, working time measurement and work improvement. The analyses presented in the article are focused on selecting the time measurement method useful from the SMED perspective. Time measurement methods and their comparison are presented in the paper. Machine learning methods are used to suggest the method of time measurement which should be applied in a particular case of workstation reorganization. A training set is developed and an example of classification is presented. Time and motion study is one of important methods of estimating machine changeover time. In the field of time study, researchers present the obtained results by using (linear) multi-linear regression models (MLR), and (non-linear) multi-layer perceptrons (MLP). The presented approach is particularly important for the enterprises which offer make-to-order products. Development of the SMED method can influence manufacturing cost reduction of customized products. In variety oriented manufacturing, SMED supports flexibility and adaptability of the manufacturing system.
Źródło:
Journal of Machine Engineering; 2018, 18, 2; 31-40
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new method for evaluation of transformer drying process using transfer function analysis and artificial neural network
Autorzy:
Firoozi, H.
Bigdeli, M.
Powiązania:
https://bibliotekanauki.pl/articles/141196.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
transformer
drying process
transfer function
artificial neural network
Opis:
Since a few years ago, there is an increasing interest for utilization of transfer functions (TF) as a reliable method for diagnosing of mechanical faults in transformer structure. However, this paper aims to develop the application of TF method in order to evaluate the drying quality of active part during the manufacturing process of transformer. To reach this goal, the required measurements are carried out on 50 MVA 132 KV/33 KV power transformer when active part is placed in the drying chamber. Two different features extracted from the measured TFs are then used as the inputs to artificial neural network (ANN) to give an estimate for required time in drying process. Results show that this new represented method could well forecast the required time. The results obtained from this method are valid for all the transformers which have the same design.
Źródło:
Archives of Electrical Engineering; 2013, 62, 1; 153-162
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers
Autorzy:
Shakir Ali Ali, Ahmed
Günal, Mustafa
Powiązania:
https://bibliotekanauki.pl/articles/2097766.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Instytut Budownictwa Wodnego PAN
Tematy:
artificial neural network
bridge pier
hydraulics
local scour
Opis:
Local scour around bridge piers impairs the stability of bridges’ structures. Therefore, a delicate estimation of the local scour depth is vital in designing the bridge piers foundations. In this research, MATLAB software was used to train artificial neural network (ANN) models with four hundred laboratory datasets from different laboratory studies, including five parameters: pier diameter, flow depth flow velocity, critical sediment velocity, sediment particle size, and equilibrium local scour depth. The outcomes present that the ANN model with the Levenberg-Marquardt algorithm and 11 nodes in the single hidden layer gives an accurate estimation better than other ANN models trained with different training algorithms based on the regression results and mean squared error values. Besides, the ANN model accurately provides predicted local scour depth and is better than linear and nonlinear regression models. Furthermore, sensitivity analysis shows that removing pier diameter from training parameters diminishes the reliability of prediction.
Źródło:
Archives of Hydro-Engineering and Environmental Mechanics; 2021, 68, 2; 87--101
1231-3726
Pojawia się w:
Archives of Hydro-Engineering and Environmental Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of artificial neural networks for the prediction of the service conditions of an elastohydrodynamic EHL contact in the presence of solid pollutant
Autorzy:
Mattallah, Sabrina
Kelaiaia, Ridha
Louahem M’Sabah, Hanane
Kerboua, Adlen
Powiązania:
https://bibliotekanauki.pl/articles/27313819.pdf
Data publikacji:
2024
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
elastohydrodynamic contact
solid pollution
artificial neural network
wear
Opis:
Lubricated mechanical mechanisms operate under service conditions influenced by several environmental parameters, and their life times may be threatened due to inappropriate use or by the presence of solid contaminants. The objective of this work is to study the effect of three operating parameters, namely: rotational speed , load and kinematic viscosity in the presence of three sizes of solid contaminants , on the degradation of an EHL contact, to predict the ranges of effects that may lead to the damage of the contacting surfaces. In our investigation, anexperimental design of nine trials is used to combine four factors with three levels each to accomplish the experimental investigation. Artificial neural network regression and the desirability function were used for the interpretation and modelling of the responses, whichare: wear , arithmetic mean height , total profile height and maximum profile height . From these methods we observed that the sand grain sizes have a significant impact on the wear and the roughness , but that viscosity has the primary influence on the variation of the roughnesses and . We also found that the quality of the predicted models is very good, with overall determination coefficients of 2 learning = 0.9985 and 2 validation = 0.9996. Several levels of degradation depending on the operating conditions are predicted using the desirability function.
Źródło:
Diagnostyka; 2024, 25, 1; art. no. 2024107
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Potential and use of the googlenet ann for the purposes of inland water ships classification
Autorzy:
Bobkowska, Katarzyna
Bodus-Olkowska, Izabela
Powiązania:
https://bibliotekanauki.pl/articles/1573774.pdf
Data publikacji:
2020
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
ship classification
image classification
geoinformatics
artificial intelligence
artificial neural network
Opis:
This article presents an analysis of the possibilities of using the pre-degraded GoogLeNet artificial neural network to classify inland vessels. Inland water authorities monitor the intensity of the vessels via CCTV. Such classification seems to be an improvement in their statutory tasks. The automatic classification of the inland vessels from video recording is a one of the main objectives of the Automatic Ship Recognition and Identification (SHREC) project. The image repository for the training purposes consists about 6,000 images of different categories of the vessels. Some images were gathered from internet websites, and some were collected by the project’s video cameras. The GoogLeNet network was trained and tested using 11 variants. These variants assumed modifications of image sets representing (e.g., change in the number of classes, change of class types, initial reconstruction of images, removal of images of insufficient quality). The final result of the classification quality was 83.6%. The newly obtained neural network can be an extension and a component of a comprehensive geoinformatics system for vessel recognition.
Źródło:
Polish Maritime Research; 2020, 4; 170-178
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie sztucznych sieci neuronowych w modelowaniu prędkości wiatru jako jednej z determinant poboru energii w budynkach
Application of neural networks in modeling wind speed as a determinant of energy consumption in buildings
Autorzy:
Jasiński, T.
Powiązania:
https://bibliotekanauki.pl/articles/362969.pdf
Data publikacji:
2018
Wydawca:
Instytut Fizyki Budowli Katarzyna i Piotr Klemm
Tematy:
sztuczne sieci neuronowe
energia elektryczna
prognozowanie
wiatr
artificial network
electrical energy
forecasting
wind
Opis:
Celem pracy była analiza możliwości wykorzystania narzędzia z obszaru sztucznej inteligencji, jakim są sztuczne sieci neuronowe (ANN) w zagadnieniach związanych z prognozowaniem poboru energii elektrycznej w budynkach. W wielu opracowaniach wykazano, że głównym źródłem popytu na energię są systemy klimatyzacji oraz systemy grzewcze (HVAC). Z tego też powodu jednym z podstawowych determinant zapotrzebowania na energię są czynniki atmosferyczne, w tym prędkość wiatru. W pracy oprócz badań literaturowych przeprowadzono również badania empiryczne w obszarze przewidywania prędkości wiatru przy użyciu ANN wykorzystujących dane archiwalne pochodzące ze stacji meteorologicznej usytuowanej na lotnisku Lublinek w Łodzi. Testom zostały poddane sieci pracujące w oparciu o architekturę perceptronu wielowarstwowego (MLP), sieci realizujące regresję uogólnioną (GRNN) oraz sieci o radialnych funkcjach bazowych (RBF). Modelowanie objęło prędkości wiatru w latach 2008-2016. Dane zostały podzielone na trzy zbiory: uczący, walidacyjny i testowy. Takie podejście umożliwiło minimalizację ryzyka przeuczenia ANN. Jednocześnie użycie jedynie najnowszych informacji w charakterze danych testowych umożliwiło opracowanie modelu, który może zostać wykorzystany w praktyce biznesowej. W pracy nie ograniczono się do poszukiwania optymalnego zbioru zmiennych objaśniających jedynie wśród danych pozyskanych bezpośrednio ze stacji meteorologicznej, lecz analizie poddano także zmienne wejściowe powstałe poprzez zastosowanie narzędzi analizy technicznej.
The paper presents possibilities to use ANN as a model predicting both - demand for energy in buildings and meteorological parameters affecting that demand such as wind speed. Empirical studies included wind speed forecasts using weather data from a meteorological station located at Lublinek Airport in Lodz. Numerous ANN types such as MLP, RBF and GRNN were tested during simulations.
Źródło:
Fizyka Budowli w Teorii i Praktyce; 2018, T. 10, nr 2, 2; 9-14
1734-4891
Pojawia się w:
Fizyka Budowli w Teorii i Praktyce
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Reducing pollution levels generated by short sea shipping : use of Bayesian networks to analyse the utilization of liquefied natural gas as an alternative fuel
Autorzy:
Molina Serrano, Beatriz
González Cancelas, Nicoleta
Soler Flores, Francisco
Powiązania:
https://bibliotekanauki.pl/articles/246442.pdf
Data publikacji:
2019
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
Bayesian networks
graph theory
artificial network
European Union
Short Sea Shipping
liquefied natural gas
Opis:
Pollution adjacent to the continent's shores has increased in the last decades, so it has been necessary to establish an energy policy to improve environmental conditions. One of the proposed solution was the search of alternative fuels to the commonly used in Short Sea Shipping to reduce pollution levels in Europe. Studies and researches show that liquefied natural gas could meet the European Union environmental requirements. Even environmental benefits are important; currently there is not significant number of vessels using it as fuel. Moreover, main target of this article is exposing result of a research in which a methodology to establish the most relevant variables in the decision to implement liquefied natural gas in Short Sea Shipping has been development using data mining. A Bayesian network was constructed because this kind of network allows to get graphically the relationships between variables and to determine posteriori values that quantify their contributions to decision-making. Bayesian model has been done using data from some European countries (European Union, Norway and Iceland) and database was generated by 35 variables classified in 5 categories. Main obtained conclusion in this analysis is that variables of transport and international trade and economy and finance are the most relevant in the decision-making process when implementing liquefied natural gas. Even more, it can be stablish that capacity of liquefied natural gas regasification terminals under construction and modal distribution of water cargo transportation continental as the most decisive variables because they are the root nodes in the obtained network.
Źródło:
Journal of KONES; 2019, 26, 1; 147-158
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The survey of soft computing techniques for reliability prediction
Autorzy:
Smoczek, J.
Powiązania:
https://bibliotekanauki.pl/articles/246835.pdf
Data publikacji:
2012
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
reliability prediction
artificial intelligence
fuzzy logic
artificial neural network
genetic algorithm
Opis:
The objective of reliability prediction is to estimate a time of upcoming nonoperational state at the current operational state of a system through real-time monitoring operational parameters and/or performances. Hence, the predictive (proactive) maintenance in industrial systems involves operational conditions monitoring and online forecasting the useful life of machines equipment to support the decision-making process in selection of the best maintenance action to be carried out. The advanced warning of the failure possibility can bring the attention of machines operators and maintenance personnel to impending danger, and facilitate planning preventive and corrective operations, as well as inventory managing. This problem has been extensively studied in many scientific works, where the predictive models are based on the data-driven approaches that can be generally divided into statistical techniques (regression, ARMA models, Bayesian probability distribution estimation, etc.), grey system theory, and soft computing methods. The artificial intelligence is frequently addressed to the predictive problem by utilizing the learning capability of artificial neural network (ANN), and possibility of nonlinear mapping using fuzzy rules-based system (FRBS) or recognizing and optimizing data-derived pattern by using evolutionary algorithms. The paper is a survey of intelligent methods for failure prediction, and delivers the review of examples of scientific works presenting the computational intelligence-based approaches to predictive problem.
Źródło:
Journal of KONES; 2012, 19, 3; 407-414
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Rainfall-river discharge modelling for flood forecasting using Artificial Neural Network (ANN)
Autorzy:
Obasi, Arinze A.
Ogbu, Kingsley N.
Orakwe, Chukwuemeka L.
Ahaneku, Isiguzo E.
Powiązania:
https://bibliotekanauki.pl/articles/292776.pdf
Data publikacji:
2020
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
artificial neural network (ANN)
rainfall
flood forecasting
river discharge
Opis:
This study is aimed at evaluating the applicability of Artificial Neural Network (ANN) model technique for river discharge forecasting. Feed-forward multilayer perceptron neural network trained with back-propagation algorithm was employed for model development. Hydro-meteorological data for the Imo River watershed, that was collected from the Anambra-Imo River Basin Development Authority, Owerri – Imo State, South-East, Nigeria, was used to train, validate and test the model. Coefficients of determination results are 0.91, 0.91 and 0.93 for training, validation and testing periods respectively. River discharge forecasts were fitted against actual discharge data for one to five lead days. Model results gave R2 values of 0.95, 0.95, 0.92, 0.96 and 0.94 for first, second, third, fourth, and fifth lead days of forecasts, respectively. It was generally observed that the R2 values decreased with increase in lead days for the model. Generally, this technique proved to be effective in river discharge modelling for flood forecasting for shorter lead-day times, especially in areas with limited data sets.
Źródło:
Journal of Water and Land Development; 2020, 44; 98-105
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predictive neural network in multipurpose self-tuning controller
Autorzy:
Bondar, Oleksiy
Powiązania:
https://bibliotekanauki.pl/articles/386771.pdf
Data publikacji:
2020
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
artificial neural network
adaptive regulator
backpropagation algorithm
system modelling
Opis:
A very important problem in designing of controlling systems is to choose the right type of architecture of controller. And it is always a compromise between accuracy, difficulty in setting up, technical complexity and cost, expandability, flexibility and so on. In this paper, multipurpose adaptive controller with implementation of artificial neural network is offered as an answer to a wide range of tasks related to regulation. The effectiveness of the approach is demonstrated by the example of an adaptive thermostat. It also compares its capabilities with those of classic PID controller. The core of this approach is the use of an artificial neural network capable of predicting the behaviour of controlled object within its known range of parameters. Since such a network, being trained, is a model of a regulated system with arbitrary precision, it can be analysed to make optimal management decisions at the moment or in a number of steps. Network learning algorithm is backpropagation and its modified version is used to analyse an already trained network in order to find the optimal solution for the regulator. Software implementation, such as graphical user interface, routines related to neural network and many other, is done using Java programming language and Processing open-source integrated development environment.
Źródło:
Acta Mechanica et Automatica; 2020, 14, 2; 114-120
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Stanowisko mocy krążącej jako system pozyskiwania danych testujących dla klasyfikatorów neuronowych
The circulating power test rig as a system of getting data test for the artificial neural network
Autorzy:
Wojnar, G.
Figlus, T.
Czech, P.
Powiązania:
https://bibliotekanauki.pl/articles/197420.pdf
Data publikacji:
2009
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
moc krążąca
klasyfikator neuronowy
artificial neural network
neural networks
Opis:
W opracowaniu przedstawiono metodologię wykorzystania stanowiska mocy krążącej jako bazy w pozyskaniu danych do walidacji klasyfikatorów neuronowych. W artykule przedstawiono metodologię pomiarów i wstępnej obróbki sygnałów zmierzonych na stanowisku FZG.
The work presents methodology of using circulating power test rig as a base of getting data set for artificial neural networks. The results of measurement used to test a neural classification system. The following paper presents a method of measuring and signal processing.
Źródło:
Zeszyty Naukowe. Transport / Politechnika Śląska; 2009, 65; 119-124
0209-3324
2450-1549
Pojawia się w:
Zeszyty Naukowe. Transport / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks
Autorzy:
Lopushniak, Vasyl Ivanovych
Hrytsuliak, Halyna Myhaylovna
Bykin, Anatoliy Viktorovych
Bordyuzha, Nadia Petryvna
Semenko, Larysa Oleksandryvna
Polutrenko, Myroslava Stepanivna
Kotsyubynska, Yulia Zinoviyivna
Powiązania:
https://bibliotekanauki.pl/articles/2028041.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
switchgrass
productivity
biomass
sewage sludge
precipitate
artificial neural network
Opis:
The article shows the possibility of using modern methods of artificial intelligence to calculate the yield of biomass of crops according to the given set input data (fertilizer doses, agrochemical parameters of the soil, productivity). The study reflects the results of testing a model of a computer program of an artificial neural network, which allowed forecasting the yield of Panicum virgatum L. (Switchgrass) depending on the joint application of fertilizers mineral and precipitate. On the basis of the calculations, the obtained model of productivity of vegetative mass of switchgrass shows a high level of forecasting efficiency (up to 97%). According to the results of experimental studies, the use of sewage sludge at a doses of 20–40 t/ha provides a dry biomass yield of Panicum virgatum L. (Switchgrass) in the range of 13.1–20.3 t/ha, which is 3.4–7.2 t/ha more than in the option without fertilizer.
Źródło:
Journal of Ecological Engineering; 2021, 22, 11; 62-71
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Journal Bearing Fault Detection Based on Daubechies Wavelet
Autorzy:
Narendiranath, B. T.
Himamshu, H. S.
Prabin, K. N.
Rama, P. D.
Nishant, C.
Powiązania:
https://bibliotekanauki.pl/articles/176955.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
journal bearing
fault diagnosis
Debauchies wavelet
artificial neural network
Opis:
Journal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. An experimental setup was used to diagnose the faults in the journal bearing. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. This was then used as input for a MATLAB code that could plot the time domain signal. This signal was then decomposed based on the wavelet transform. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. Then the Artificial Neural Networks (ANN) is used to classify faults. The network is trained based on data already collected and then it is tested based on random data points. ANN was able to classify the faults with the classification rate of 85.7%. Thus, the test process for unseen vibration data of the trained ANN combined with ideal output target values indicates high success rate for automated bearing fault detection.
Źródło:
Archives of Acoustics; 2017, 42, 3; 401-414
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid, finite element-artificial neural network model for composite materials
Zastosowanie sztucznych sieci neuronowych w modelowaniu numerycznym kompozytów przy pomocy metody elementów skończonych
Autorzy:
Lefik, M.
Powiązania:
https://bibliotekanauki.pl/articles/281993.pdf
Data publikacji:
2004
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
artificial neural network
composite materials
self-learning FE method
Opis:
An application of Artificial Neural Networks for a definition of the effective constitutive law for a composite is described in the paper. First, a classical homogenisation procedure is directly interpreted with a use of this numerical tool. Next, a self-learning Finite Element code (FE with ANN inside) is used in the case when the effective constitutive law is deduced from a numerical experiment (substituting here a purely phenomenological approach). The new contribution to the classical self-learning procedure consists of its adaptation to a case of a non-monotonic loading (non-to-one load-deformation curve). This new ability of the method is principally due to the incremental form of the constitutive equation and the respective scheme of the neural network structure. Also an organisation of a constitutive data-base containing learning patterns is suitably modified. It is shown by examples that the training process is very quick. The error of this method is smaller, comparing to other schemes of data acquisition.
W artykule opisano zastosowanie sztucznych sieci neuronowych do określenia efektywnego związku konstytutywnego dla kompozytów. To narzędzie numeryczne użyte zostało dwojako: do bezpośredniego zapisu wyników otrzymanych w ramach klasycznej metody homogenizacji oraz do wnioskowania o własnościach efektywnych na podstawie eksperymentu numerycznego (zastępującego eksperyment rzeczywisty) wykonanego na małej, lecz reprezentatywnej próbce kompozytu. W tym drugim przypadku zastosowano schemat "samouczącego się" programu metody elementów skończonych, w którym związek konstytutywny opisany jest siecią neuronową. Schemat ten zaadaptowano tak, że może być użyty w przypadku obciążeń niemonotonicznych oraz wtedy, gdy zależność: miara odkształcenia-miara naprężenia nie jest wzajemnie jednoznaczna. Te nowe możliwości uzyskane zostały dzięki przedstawieniu związku konstytutywnego w formie przyrostowej oraz opracowania odpowiedniej do tego budowy sieci neuronowej. Schemat "samouczącego się" programu MES charakteryzuje się tym, że proces formułowania nieznanego związku konstytutywnego jest szybki, a zgodność modelu numerycznego z eksperymentem większa niż dla innych metod.
Źródło:
Journal of Theoretical and Applied Mechanics; 2004, 42, 3; 539-563
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Logical network design of microbearing systems
Autorzy:
Wierzcholski, K.
Powiązania:
https://bibliotekanauki.pl/articles/242043.pdf
Data publikacji:
2011
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
artificial neural network
propositional calculus application
optimum of solutions
Opis:
This paper presents the some applications of logical network analysis in topological form as an Artificial Neural Networks (ANN) intelligence component implemented regard to the optimum calculations of micro-bearing operating parameters such as hydrodynamic pressure, carrying load capacity, and optimum measurements of friction forces, friction coefficient and micro-bearing wear. Efficient functioning of slide micro-bearings systems require to choice the proper journal shapes, bearing materials, roughness of bearing surfaces and many other features to which belongs capability to the processes and control. Artificial intelligence of micro-bearing leads to the creating and indicating of the network logical models to describe most simple and most proper topological graphical schemes presenting the design of anticipated processes. Application of the logical network analysis into the micro-bearing HDD design is the subject-matter of this paper. Mechanism of neuron activity, basic scheme of detection system in modern of Atomic Force Microscope, tip radius estimation, input impulse, tribo-topology logical network scheme mechanism, tribo-topology logical network analysis scheme mechanism, the pressure distributions in cylindrical micro-bearings caused by the rotation in circumferential direction and magnetic field, the view from the film origin and from film end are presented in the paper.
Źródło:
Journal of KONES; 2011, 18, 2; 455-462
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Model development of the external friction of granular vegetable materials on the basis of artificial neural networks
Autorzy:
Francik, S.
Fraczek, J.
Powiązania:
https://bibliotekanauki.pl/articles/25205.pdf
Data publikacji:
2001
Wydawca:
Polska Akademia Nauk. Instytut Agrofizyki PAN
Tematy:
granular vegetable material
artificial neural network
external friction
vegetable
Źródło:
International Agrophysics; 2001, 15, 4
0236-8722
Pojawia się w:
International Agrophysics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A selected problem of the structure optimization and decomposition of the artificial neural network with cross-forward connections
Autorzy:
Płaczek, S.
Powiązania:
https://bibliotekanauki.pl/articles/97313.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
artificial neural network
structure optimization
decomposition
coordination
cross connection
Opis:
The problem of an Artificial Neural Network (ANN) structure optimization is related to the definition of the optimal number of hidden layers and the distribution of neurons between layers depending on a selected optimization criterion and inflicted constrains. Using a hierarchical structure is an accepted default way of defining an ANN structure. The following article presents the resolution of the optimization problem. The function describing the number of subspaces is given, and the minimum number of layers, as well as the distribution of neurons between layers, shall be found. The structure can be described using different methods, mathematical tools, and software or/and technical implementation. The ANN decomposition into hidden and output layers - the first step to build a two-level learning algorithm for cross-forward connections structure - is described, too.
Źródło:
Computer Applications in Electrical Engineering; 2014, 12; 597-608
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial Neural Networks as a Tool for Supporting a Moulding Sand Control System Based on the Dependency between Selected Moulding Sand Properties
Autorzy:
Mrzygłód, Barbara
Jakubski, Jarosław
Opaliński, Andrzej
Regulski, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/24201264.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
artificial neural network
decision support
green moulding sand
compactibility
Opis:
The article presents the potential for using artificial neural networks to support decisions related to the rebonding of green moulding sand. The basic properties of the moulding sand tested in foundries are discussed, especially compactibility as it gives the most information about the quality of green moulding sand. First, the data that can predict the compactibility value without the need for testing are defined. Next, a method for constructing an artificial neural network is presented and the network model which produced the best results is analysed. Additionally, two applications were designed to allow the investigation results to be searchable by determining the range of values of the moulding sand parameters.
Źródło:
Journal of Casting & Materials Engineering; 2023, 7, 2; 15--21
2543-9901
Pojawia się w:
Journal of Casting & Materials Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural network and artificial immune algorithms for the classification of medical data series
Sieci neuronowe i sieci immunologiczne dla rozpoznawania przypadków medycznych
Autorzy:
Wajs, W.
Powiązania:
https://bibliotekanauki.pl/articles/282174.pdf
Data publikacji:
2012
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
sztuczne sieci neuronowe
sieci immunologiczne
SVM
BPD
artificial neural network
immunological network
Opis:
This paper describes the applicability of artificial immune algorithms. Medical data series classification technique by Artificial Immune Algorithm is used for Neural Network Algorithm input data definitions. Artificial Immune Algorithms is created and trained for the purpose of Arterial Blood Gas parameters classification: pH, PaCO2, PaO2, HCO3. The main goal of this paper is to develop a artificial neural network technique for Arterial Blood Gases short-term prediction. The main question that is considered is how to predict some dynamic parameters that describe blood gases nature. A model of a physical system has an error associated with its predictions due to the dependences of the physical system's output on uncontrollable and unobservable quantities. The use of artificial methods creates the possibilities of obtaining some parameter values on the proper level of probability. This would provide a direct feedback to the clinical staff about the progress of a patient, the success of individual treatments, and quality of care as well as predicting blood gas value.
Dla rozpoznawania przypadków chorobowych, które są opisane numerycznymi danymi wykorzystano metody sztucznej inteligencji. W pracy wykorzystano dwie metody: metodę sztucznych sieci neuronowych oraz metodę sztucznych sieci immunologicznych. Przedstawiono wyniki uzyskane tymi metodami w odniesieniu do przypadków dysplazji oskrzelowo płucnej dla dzieci, których waga była poniżej 1500 g.
Źródło:
Automatyka / Automatics; 2012, 16, 1; 89-96
1429-3447
2353-0952
Pojawia się w:
Automatyka / Automatics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural network simulation in running of acetic acid synthesis unit while start-up
Nejjroetevoe modelirovanie dlja upravlenija kolonnojj sinteza uksusnojj kisloty v period puska
Autorzy:
Porkuian, O.
Samojlova, Z.
Powiązania:
https://bibliotekanauki.pl/articles/792304.pdf
Data publikacji:
2013
Wydawca:
Komisja Motoryzacji i Energetyki Rolnictwa
Tematy:
neural network
artificial neural network
automated control system
acetic acid
MATLAB software
Źródło:
Teka Komisji Motoryzacji i Energetyki Rolnictwa; 2013, 13, 3
1641-7739
Pojawia się w:
Teka Komisji Motoryzacji i Energetyki Rolnictwa
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial neural network (ANN) modelling to estimate bubble size from macroscopic image and object features
Autorzy:
Vinnett, Luis
León, Roberto
Mesa, Diego
Powiązania:
https://bibliotekanauki.pl/articles/29552038.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
machine learning
artificial neural network
flotation
bubble size
Sauter diameter
Opis:
Bubble size measurements in aerated systems such as froth flotation cells are critical for controlling gas dispersion. Commonly, bubbles are measured by obtaining representative photographs, which are then analyzed using segmentation and identification software tools. Recent developments have focused on enhancing these segmentation tools. However, the main challenges around complex bubble cluster segmentation remain unresolved, while the tools to tackle these challenges have become increasingly complex and computationally expensive. In this work, we propose an alternative solution, circumventing the need for image segmentation and bubble identification. An Artificial Neural Network (ANN) was trained to estimate the Sauter mean bubble size (D32) based on macroscopic image features obtained with simple and inexpensive image analysis. The results showed excellent prediction accuracy, with a correlation coefficient, R, over 0.998 in the testing stage, and without bias in its error distribution. This machine learning tool paves the way for robust and fast estimation of bubble size under complex bubble images, without the need of image segmentation.
Źródło:
Physicochemical Problems of Mineral Processing; 2023, 59, 5; art. no. 185759
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy
Autorzy:
Alemam, Mustafa K.
Yong, Bin
Sani-Mohammed, Abubakar
Powiązania:
https://bibliotekanauki.pl/articles/27314479.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Centrum Badań Kosmicznych PAN
Tematy:
digital elevation model
GIS
artificial neural network
interpolation methods
SRTM
Opis:
The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.
Źródło:
Artificial Satellites. Journal of Planetary Geodesy; 2023, 58, 3; 122--170
2083-6104
Pojawia się w:
Artificial Satellites. Journal of Planetary Geodesy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural modeling of the electric power stock market in usage of MATLAB and Simulink tools for the day ahead market data
Autorzy:
Ruciński, D.
Tchórzewski, J.
Powiązania:
https://bibliotekanauki.pl/articles/94831.pdf
Data publikacji:
2016
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
neuronal modelling
MATLAB
Simulink environment
simulation research
artificial neural network
Opis:
The work contains selected results of the modelling of neural Electric Power Exchange (EPE) in Poland. For modelling EPE system, artificial neural network (ANN) was constructed. ANN was learned and tested using of the next day market data. Generated neural model was used for simulation tests and susceptibility tests. Suitable model was implemented in Simulink. As a result of simulation tests and susceptibility testing a lot of interesting research results were obtained.
Źródło:
Information Systems in Management; 2016, 5, 2; 215-226
2084-5537
2544-1728
Pojawia się w:
Information Systems in Management
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predykcja zmian temperatury dla złoża kompostu w zależności od stopnia napowietrzenia przy pomocy sztucznych sieci neuronowych
Prediction of temperature changes for compost bed depending on aeration degree, carried out using artificial neural networks
Autorzy:
Neugebauer, M.
Piechocki, J.
Sołowiej, P.
Powiązania:
https://bibliotekanauki.pl/articles/288900.pdf
Data publikacji:
2010
Wydawca:
Polskie Towarzystwo Inżynierii Rolniczej
Tematy:
kompostowanie
sztuczna sieć neuronowa
napowietrzanie
composting
artificial neural network
aeration
Opis:
Efektywność procesu kompostowania zależy od wielu czynników. Jednym z nich jest intensywność napowietrzania złoża kompostu. Również zmiana temperatury procesu w czasie jest ważnym czynnikiem warunkującym jakość uzyskanego kompostu oraz wpływa również na czas trwania procesu kompostowania. W ramach przeprowadzonych badań kompostowano materiał biologiczny pochodzenia rolniczego dla różnych wartości intensywności napowietrzania. W ramach badań mierzono również zmiany temperatury w złożu kompostu w czasie kompostowania. Uzyskane dane zostały następnie wykorzystane do uczenia sztucznych sieci neuronowych (SSN). Wybrane SSN (o najniższych wartościach błędów) zostały następnie wykorzystane do przewidywania zmian temperatury w złożu kompostu i czasu trwania procesu kompostowania dla innych wartości napowietrzania złoża.
Composting process intensity depends on many determinants. One of them is compost bed aeration intensity. Also, process temperature change in time is an important factor determining quality of obtained compost. Moreover, it affects composting process duration. The scope of carried out research involved composting biological material of agricultural origin for different aeration intensity values. Moreover, completed tests covered measuring temperature changes in compost bed during composting. Then, obtained data was used to teach artificial neural networks (ANN). The selected ANN (with lowest error values) were then used to predict temperature changes in compost bed and composting process duration for other bed aeration values.
Źródło:
Inżynieria Rolnicza; 2010, R. 14, nr 3, 3; 151-157
1429-7264
Pojawia się w:
Inżynieria Rolnicza
Dostawca treści:
Biblioteka Nauki
Artykuł
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