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    Wyświetlanie 1-10 z 10
    Tytuł:
    Prediction of industrial pollution by radial basis function networks
    Autorzy:
    Djebbri, N.
    Rouainia, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/207579.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    forecasting
    RBF
    artificial neural network
    pollution
    prognozowanie
    sztuczna sieć neuronowa
    zanieczyszczenie
    Opis:
    Atmospheric pollution has been receiving a significant interest for several decades since industries cause more and more pollution. Thanks to the development of many prediction techniques, scientists and industries are focusing more on pollution prediction. The aim of this work is to predict the two pollutant concentrations (NOx and CO) in industrial sites by a modified radial basis function (RBF) based neural network. The modification considered the spread parameter h of the activation function in the RBF network. In order to get the best network, the variations of this parameter for three cases were considered. In the first case, only pollutants concentrations variables were used, while in the second one, only the meteorological variables were utilized. In the third case, pollutants' concentrations were connected with meteorological variables. Based on calculation errors, the best model that ensures the best monitoring of pollutants concentration could be identified.
    Źródło:
    Environment Protection Engineering; 2018, 44, 3; 153-164
    0324-8828
    Pojawia się w:
    Environment Protection Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of Impact Resistance Properties of Concrete Using Radial Basis Function Networks
    Autorzy:
    Yazici, S.
    Inan Sezer, G.
    Sezer, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/1031254.pdf
    Data publikacji:
    2017-09
    Wydawca:
    Polska Akademia Nauk. Instytut Fizyki PAN
    Tematy:
    81.70.Bt
    79.20.Ap
    07.05.Mh
    Opis:
    This study presents an investigation of the prediction of impact resistance of steel-fiber-reinforced concrete and ordinary concrete specimens. In the experimental part of this study, parameters identifying impact resistance of various concrete mixtures were determined using an impact test machine, in accordance with ACI Committee 544. For this aim, concrete specimens containing three different aggregates (basalt, limestone and natural aggregate) were cured in water at 20°C for 28 days. After curing impact resistance tests were performed on specimens having compressive strength values between 20 and 50 MPa, to determine the blows to initial crack and failure. The specimens were also subjected to splitting tensile strength and ultrasonic pulse velocity tests. Initially, using blows to initial crack and failure, many attempts were made to classify the impact resistance of different types of concrete in terms of the origin of used aggregate, strength properties or ultrasonic pulse velocity, however, this made no sense. The specimens could only be classified in terms of steel fiber presence. Therefore, radial basis function network, which belongs to another kind of unsupervised classifier network, was used to estimate the two above-mentioned impact resistance parameters. In this scope, independent from aggregate origin used in preparation of specimens, compressive strength, splitting tensile strength and ultrasonic pulse velocity of the specimens were used to predict the impact resistance parameters of the concrete specimens. The results revealed that three listed parameters can be used for estimation of blows to formation of initial crack and failure. Scatter plots, root mean square error and absolute value of average residual parameters were used to verify the errors in predictions, which were very low, compared with the uncertainty in test and ambiguity of data in hand.
    Źródło:
    Acta Physica Polonica A; 2017, 132, 3; 1036-1040
    0587-4246
    1898-794X
    Pojawia się w:
    Acta Physica Polonica A
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks
    Autorzy:
    Ahmida, Z.
    Charef, A.
    Becerra, V. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/908523.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system nieliniowy
    sterowanie optymalne
    radialna funkcja bazowa
    sieć neuronowa
    regulacja predykcyjna
    sterowanie wyprzedzające
    nonlinear systems
    optimal control
    radial basis functions
    neural networks
    predictive control
    feedforward control
    Opis:
    A controller architecture for nonlinear systems described by Gaussian RBF neural networks is proposed. The controller is a stabilising solution to a class of nonlinear optimal state tracking problems and consists of a combination of a state feedback stabilising regulator and a feedforward neuro-controller. The state feedback stabilising regulator is computed online by transforming the tracking problem into a more manageable regulation one, which is solved within the framework of a nonlinear predictive control strategy with guaranteed stability. The feedforward neuro-controller has been designed using the concept of inverse mapping. The proposed control scheme is demonstrated on a simulated single-link robotic manipulator.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2005, 15, 3; 369-381
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads
    Autorzy:
    Mansourkhaki, A.
    Berangi, M.
    Haghiri, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/124655.pdf
    Data publikacji:
    2018
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    MLP
    RBF
    neural network
    noise prediction
    road traffic noise
    Opis:
    Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effects on humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to find new approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LAeq) by measuring the traffic volume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficient of determination (R2) and the Mean Squared Error (MSE). Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.
    Źródło:
    Journal of Ecological Engineering; 2018, 19, 1; 113-121
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of prototype selection algorithms used in construction of neural networks learned by SVD
    Autorzy:
    Jankowski, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/330020.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    radial basis function network
    extreme learning machine
    kernel method
    prototype selection
    machine learning
    k nearest neighbours
    radialna funkcja bazowa
    metoda jądrowa
    uczenie maszynowe
    metoda k najbliższych sąsiadów
    Opis:
    Radial basis function networks (RBFNs) or extreme learning machines (ELMs) can be seen as linear combinations of kernel functions (hidden neurons). Kernels can be constructed in random processes like in ELMs, or the positions of kernels can be initialized by a random subset of training vectors, or kernels can be constructed in a (sub-)learning process (sometimes by k-means, for example). We found that kernels constructed using prototype selection algorithms provide very accurate and stable solutions. What is more, prototype selection algorithms automatically choose not only the placement of prototypes, but also their number. Thanks to this advantage, it is no longer necessary to estimate the number of kernels with time-consuming multiple train-test procedures. The best results of learning can be obtained by pseudo-inverse learning with a singular value decomposition (SVD) algorithm. The article presents a comparison of several prototype selection algorithms co-working with singular value decomposition-based learning. The presented comparison clearly shows that the combination of prototype selection and SVD learning of a neural network is significantly better than a random selection of kernels for the RBFN or the ELM, the support vector machine or the kNN. Moreover, the presented learning scheme requires no parameters except for the width of the Gaussian kernel.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 719-733
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Lan interconnection unit based on an artificial neural network
    Autorzy:
    Jalab, Hamid A.
    Powiązania:
    https://bibliotekanauki.pl/articles/1955324.pdf
    Data publikacji:
    2006
    Wydawca:
    Politechnika Gdańska
    Tematy:
    LAN bridge
    neural networks
    radial basis function (RBF)
    Opis:
    This paper presents the design of an intelligent interconnection unit based on an artificial neural network (ANN), used when two local area networks (LAN) with different IEEE 802 standard protocols are connected. The proposed ANN is used to activate execution of suitable procedures bridging 802.X LAN and 802.Y LAN.
    Źródło:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2006, 10, 3; 339-346
    1428-6394
    Pojawia się w:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Vehicles Classification Using the HRBF Neural Network
    Klasyfikacja pojazdów z wykorzystaniem sieci neuronowej HRBF
    Autorzy:
    Wantoch-Rekowski, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/305921.pdf
    Data publikacji:
    2011
    Wydawca:
    Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
    Tematy:
    sieci neuronowe
    klasyfikacja sieci
    zbiór uczący
    Hyper Radial Basis Function network HRBF
    neural networks
    networks classification
    learning set
    HRBF
    Opis:
    The paper presents the problem of using a neural network for military vehicle classification on the basis of ground vibration. One of the main elements of the system is a unit called the geophone. This unit allows to measure the amplitude of ground vibration in each direction for a certain period of time. The value of the amplitude is used to fix the characteristic frequencies of each vehicle. If we want to fix the main frequency it is necessary to use the Fourier transform. In this case the fast Fourier transform FFT was used. Since the neural network (Hyper Radial Basis Function network) was used, a learning set has to be prepared. Please find the attached results of using the HRBF neural network, which include: examples of learning, validation and test sets, the structure of the networks and the learning algorithm, learning and testing results.
    W opracowaniu przedstawiono zagadnienie wykorzystania sieci neuronowej do klasyfikacji określonych typów pojazdów na podstawie analizy amplitudy drgań gruntu. Jednym z elementów systemu do pomiaru amplitudy drgań gruntu jest geofon. Umożliwia on pomiar amplitudy drgań gruntu w wybranym kierunku dla określonego przedziału czasu. Wartość wyznaczonej amplitudy wykorzystywana jest do wyznaczenia charakterystycznych częstotliwości drgań dla poszczególnych pojazdów. Do wyznaczenia charakterystycznych częstotliwości wykorzystywana jest transformata Fouriera FFT. Do klasyfikacji wykorzystana została sieć neuronowa z radialną funkcją aktywacji, dlatego też wymagane jest przygotowanie odpowiedniego zbioru uczącego. W opracowaniu przedstawiono wyniki użycia sieci HRBF. Przedstawiono strukturę oraz zawartość zbioru uczącego.
    Źródło:
    Biuletyn Instytutu Systemów Informatycznych; 2011, 7; 47-52
    1508-4183
    Pojawia się w:
    Biuletyn Instytutu Systemów Informatycznych
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Face Recognition Using Canonical Correlation, Discrimination Power, and Fractional Multiple Exemplar Discriminant Analyses
    Autorzy:
    Hajiarbabi, M.
    Agah, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/384779.pdf
    Data publikacji:
    2015
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    face recognition
    Canonical Correlation Analysis
    Discrimination Power Analysis
    Multiple Exemplar Discriminant Analysis
    Radial Basis Function neural
    networks
    Opis:
    Face recognition is a biometric identification method which compared to other methods, such as finger print identification, speech, signature, hand written and iris recognition is shown to be more noteworthy both theoretically and practically. Biometric identification methods have various applications such as in film processing, control access networks, among many. The automatic recognition of a human face has become an important problem in pattern recognition, due to (1) the structural similarity of human faces, and (2) great impact of factors such as illumination conditions, facial expression and face orientation. These have made face recognition one of the most challenging problems in pattern recognition. Appearance-based methods are one of the most common methods in face recognition, which can be categorized into linear and nonlinear methods. In this paper face recognition using Canonical Correlation Analysis is introduced, along with the review of the linear and nonlinear appearance-based methods. Canonical Correla- tion Analysis finds the linear combinations between two sets of variables which have maximum correlation with one another. Discriminant Power analysis and Fractional Multiple Discriminant Analysis has been used to extract features from the image. The results provided in this paper show the advantage of this method compared to other methods in this field.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2015, 9, 4; 18-27
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The Analytical and Artificial Intelligence Methods to Investigate the Effects of Aperture Dimension Ratio on Electrical Shielding Effectiveness
    Autorzy:
    Basyigit, Ibrahim Bahadir
    Dogan, Habib
    Powiązania:
    https://bibliotekanauki.pl/articles/226583.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    electromagnetic shielding
    electromagnetic
    compatibility
    apertures
    multilayer perceptron
    radial basis
    function networks
    Opis:
    This paper presents that the effect of single aperture size of metallic enclosure on electrical shielding effectiveness (ESE) at 0 – 1 GHz frequency range has been investigated by using both Robinson’s analytical formulation and artificial neural networks (ANN) methods that are multilayer perceptron (MLP) networks and a radial basis function neural network (RBFNN). All results including measurement have been compared each other in terms of aperture geometry of metallic enclosure. The geometry of single aperture varies from square to rectangular shape while the open area of aperture is fixed. It has been observed that network structure of MLP 3-40-1 in modeling with ANN modeled with fewer neurons in the sense of overlapping of faults and data and modeled accordingly. In contrast, the RBFNN 3-150-1 is the other detection that the network structure is modeled with more neurons and more. It can be seen from the same network-structured MLP and RBFNN that the MLP modeled better. In this paper, the impact of dimension of rectangular aperture on shielding performance by using RBFNN and MLP network model with ANN has been studied, as a novelty.
    Źródło:
    International Journal of Electronics and Telecommunications; 2019, 65, 3; 359-365
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of MLP and RBF Neural Networks in the Task of Classifying the Diameters of Water Pipes
    Autorzy:
    Gvishiani, Zurab
    Dawidowicz, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/2174907.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Koszalińska. Wydawnictwo Uczelniane
    Tematy:
    water distribution system
    hydraulic calculation
    selection of diameter
    water pipe
    artificial neural network
    radial basis function
    multilayer perceptron
    Opis:
    Hydraulic calculations of water distribution systems are currently performed using computer programs. In addition to the basic calculation procedure, modules responsible for evaluating the obtained calculation results are introduced more and more often into the programs. This article presents the results of research on artificial neural networks with a radial base function (RBF) and a multilayer perceptron (MLP), aimed at determining whether they can be used to model the relationship between the variables describing the computational section of the water distribution system and the diameter of the water pipe. The classification capabilities of the RBF and MLP networks were analyzed according to the number of neurons in the hidden layer of the network. A comparative analysis of RBF networks with multilayer perceptron (MLP) networks was performed. The results showed that the MLP networks have much better classification properties and are better suited for the task of assessing the selected diameters of the water pipes.
    Źródło:
    Rocznik Ochrona Środowiska; 2022, 24; 505--519
    1506-218X
    Pojawia się w:
    Rocznik Ochrona Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-10 z 10

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