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Wyświetlanie 1-15 z 393
Tytuł:
“P” coordinator scheme and interaction prediction principle in hierarchical structure of ANN
Autorzy:
Płaczek, S.
Powiązania:
https://bibliotekanauki.pl/articles/97277.pdf
Data publikacji:
2015
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
Artificial Neural Network (ANN)
hierarchy
decomposition
coordination
coordination principle
P-regulator
feedback principle
Opis:
When implementing the hierarchical structure [4][5] of the learning algorithm of an Artificial Neural Network (ANN), two very important questions have to be solved. The first one is connected with the selection of the broad coordination principle. In [1], three different principles are described. They vary with regard to the degree of freedom for the first-level tasks. The second problem is connected with the coordinator structure or, in other words, the coordination algorithm. In the regulation theory, the process of finding the coordinator structure is known as the feedback principle. The simplest regulator structure (scheme) is known as the proportional regulator – “P” regulator. In the article, the regulator structure and its parameters are analysed as well as their impact on the learning process quality.
Źródło:
Computer Applications in Electrical Engineering; 2015, 13; 319-329
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparative analysis of artificial neural network predictive and multiple linear regression models for ground settlement during tunnel construction
Autorzy:
Zou, Baoping
Chibawe, Musa
Hu, Bo
Deng, Yansheng
Powiązania:
https://bibliotekanauki.pl/articles/27312113.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
budowa
tunel
osiadanie gruntu
regresja liniowa wielokrotna
sieć neuronowa sztuczna
tunnel
construction
ground settlement
multiple linear regression
artificial neural network
Opis:
Ground settlement during and after tunnelling using TBM results in varying dynamic and static load action on the geo-stratum. It is an undesirable effect of tunnel construction causing damage to the surface and subsurface infrastructure, safety risk, and increased construction cost and quality issues. Ground settlement can be influenced by several factors, like method of tunnelling, tunnel geometry, location of tunnelling machine, machine operational parameters, depth & its changes, and mileage of recording point from starting point. In this study, a description and evaluation of the performance of the artifcial neural network (ANN) was undertaken and a comparison with multiple linear regression (MLR) was carried out on ground settlement prediction. The performance of these models was evaluated using the coefficient of determination R2, root mean square error (RMSE) and mean absolute percentage error (MAPE). For ANN model, the R2, RMSE and MAPE were calculated as 0.9295, 4.2563 and 3.3372, respectively, while for MLR, the R2, RMSE and MAPE, were calculated as 0.5053, 11.2708, 6.3963 respectively. For ground settlement prediction, both ANN and MLR methods were able to predict significantly accurate results. It was further noted that the ANN performance was higher than that of the MLR.
Źródło:
Archives of Civil Engineering; 2023, 69, 2; 503--515
1230-2945
Pojawia się w:
Archives of Civil Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparison of the fuel consumption characteristic in dynamic states with the general characteristic of the combustion engine
Porównanie charakterystyki zużycia paliwa w dynamicznych stanach pracy z charakterystyką ogólną silnika spalinowego
Autorzy:
Bera, P.
Powiązania:
https://bibliotekanauki.pl/articles/133995.pdf
Data publikacji:
2015
Wydawca:
Polskie Towarzystwo Naukowe Silników Spalinowych
Tematy:
dynamic states
fuel consumption
artificial neural network
stany dynamiczne
zużycie paliwa
sztuczne sieci neuronowe
Opis:
The paper presents a comparison of the fuel consumption characteristic in dynamic states with the general characteristic. It was proven that the use of the general characteristic to calculate the fuel consumption in dynamic operating states is limited because under certain conditions the results are incorrect. This is due to the fact that this characteristic is based on data obtained from the engine test bench measurements in static states. In contrast, the characteristic in the dynamic states is determined on the basis of data from both static and dynamic states characterized by variable engine speed and torque. It reflects the nature of the internal combustion engine much better because it takes into account the specificity of its work. Development of such characteristic is possible through the use of artificial neural network trained in a supervised mode to analyze the data collected during tests on the engine test bench. The purpose of this paper is to quantitatively compare the results obtained by each of the characteristics in different operating states of the engine. The analysis takes into account both the trajectory between the successive work points as well as time in which the change occurs.
W artykule przedstawiono porównanie charakterystyki zużycia paliwa w stanach dynamicznych z charakterystyką ogólną. Wykazano, że wykorzystanie charakterystyki ogólnej do obliczania zużycia paliwa w dynamicznych stanach pracy jest ograniczone, gdyż w pewnych przypadkach daje wyniki obarczone istotnymi błędami. Wynika to z faktu, że jest ona wyznaczana w oparciu o dane z pomiarów w stanach statycznych na hamowni silnikowej. Inaczej jest w przypadku charakterystyki w stanach dynamicznych, która jest sporządzana w oparciu o dane pomiarowe ze stanów zarówno statycznych jak i dynamicznych. Wierniej oddaje ona charakter pracy samochodowego silnika spalinowego, gdyż uwzględnia specyfikę zjawisk w nim zachodzących. W artykule wykazano, że wykorzystanie sztucznej sieci neuronowej uczonej w sposób nadzorowany do analizy danych zgromadzonych w trakcie badań na hamowni silnikowej pozwala na opracowanie takiej charakterystyki. Przedstawiono także ilościowe porównanie wyników otrzymywanych przez każdą z charakterystyk w różnych stanach pracy silnika spa-linowego. Przeprowadzona analiza uwzględnia zarówno trajektorię przejść między kolejnymi punktami pracy jak i czas w którym ta zmiana zachodzi.
Źródło:
Combustion Engines; 2015, 54, 3; 726-731
2300-9896
2658-1442
Pojawia się w:
Combustion Engines
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A durability analysis of forging tools for different operating conditions with application of a decision support system based on artificial neural networks (ANN)
Analiza trwałości narzędzi kuźniczych dla różnych warunków eksploatacji z wykorzystaniem systemu wspomagania decyzji opartego o sztuczne sieci neuronowe
Autorzy:
Hawryluk, M.
Mrzygłód, B.
Powiązania:
https://bibliotekanauki.pl/articles/1365633.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
sztuczna sieć neuronowa
system wspomagania decyzji
kucie matrycowe
trwałość narzędzi kuźniczych
zużycie i mechanizmy niszczące
artificial neural network
decision support system
die forging
durability of forging tools
wear and destructive mechanisms
Opis:
W pracy przedstawiono wyniki badań, dotyczące, procentowego udziału mechanizmów niszczących dla dwóch typowych wariantów eksploatacji narzędzi kuźniczych: smarowanych i chłodzonych oraz bez smarowania. Prezentowane wyniki pochodzą z opracowanego przez autorów systemu wspomagania decyzji (SEPEK-2) działającego w oparciu o sztuczną sieć neuronową. Wiedza o analizowanym zagadnieniu trwałości narzędzi kuźniczych, potrzebna do procesu uczenia sztucznej sieci neuronowej zawarta była w zestawie danych uczących, pochodzących z kompleksowych badań, zrealizowanych w warunkach przemysłowych. Zestaw danych uczących obejmował zbiór 450 rekordów wiedzy. W pracy przestawiono proces pozyskiwania wiedzy, przyjętą architekturę sieci neuronowej oraz parametry opracowanej sieci. Przeprowadzona globalna analiza wyników generowanych przez opracowany system, dla trwałości traktowanej jako zwiększająca się liczba odkuwek (od 0 do 25000 sztuk), wykazała że dla narzędzi smarowanych i chłodzonych dominującym mechanizmem jest zmęczenie cieplno-mechaniczne, a nie zużycie ścierne, które rzeczywiście dominuje w procesach kucia dla narzędzi niechłodzonych i niesmarowanych. Należy podkreślić, że zdecydowana większość opracowań z tego obszaru przypisuje, że to zużycie ścierne jest dominujące, a jak wykazały wyniki badań i analiz dla wybranych reprezentatywnych procesów kucia, przy wykorzystaniu systemu wspomagania decyzji opartego o SNN, to zmęczenie cieplno-mechaniczne jest dominujące w tych procesach. Jednakże ze względu na łatwą mierzalność oraz popularnie stosowane modele zużycia ściernego, bazujące na modelu Archarda, to właśnie zużyciu ściernemu przypisuję się największy udział, choć w rzeczywistości dla narzędzi smarowanych i chłodzonych zmęczenie cieplno-mechaniczne wzmaga ów efekt przypisywany zużyciu ściernemu. Natomiast ogólnie przyjęty pogląd jest słuszny, w przypadku narzędzi niesmarowanych. co potwierdziły także analizy przy wykorzystaniu SNN.
The paper presents the results of research concerning the percentage participation of destructive mechanisms for two typical variants of exploitation of forging tools: lubricated and cooled, and without lubrication. Discussed results come from the developed by the authors the decision support system (SEPEK-2) based on artificial neural network. The knowledge about the durability of forging tools needed for learning artificial neural network was included in the training data set, from comprehensive studies, carried out in industrial conditions. Set of training data set included 450 records of knowledge. The paper presents the process of acquiring knowledge, adopted neural network architecture and parameters developed network. Carried out a global analysis of the results generated by the developed system for the durability of forging tools treated as the maximum number of produced forgings to their destruction (from 0 to 25,000 items), showed that for the lubricated and cooling tools the dominant mechanism is thermo-mechanical fatigue, and do not abrasive wear, which actually dominates in the process of forging tools for uncooled and unlubricated tools. It should be emphasized that the overwhelming majority of studies in this area is attributed that to abrasive wear is dominant, and as shown by the results of research and analysis for the selected representative forging processes, with the use of decision support system based on ANN, the fatigue a thermo-mechanical is dominant in these processes. However, due to the easy measurability and commonly used models wear, based on the model of Archard, it is abrasive wear assigned the largest participation. In fact, for the tool lubricated and cooled tools a thermo-mechanical fatigue intensifies this effect attributed to abrasive wear. While the generally accepted view is correct, in the case of tools unlubricated, as confirmed by the analysis using ANN.
Źródło:
Eksploatacja i Niezawodność; 2017, 19, 3; 338-348
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Life Factor Approach to the Yield Prediction: a Comparison with a Technological Approach in Reliability and Accuracy
Autorzy:
Lykhovyd, Pavlo
Powiązania:
https://bibliotekanauki.pl/articles/124852.pdf
Data publikacji:
2019
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
artificial neural network
life factor
multiple linear regression
technological factor
yield modelling
Opis:
There are a number of various approaches to the development of yield predictive models in agriculture. One of the most popular ones is based on the yield modeling from the parameters of crop cultivation technology. However, there is another view on the yield prediction models, which is based on the use of life factors as yielding parameters. Our study is devoted to the comparison of a conventional technological approach to the yield prediction with a less prevalent approach of life factor based yield modeling. The testing of two approaches was performed by using the yielding data of sweet corn cultivated in the field trials under the drip-irrigated conditions of the Southern Ukraine, under the different technological treatments, viz. plowing depth, nutrition, and crop density. We developed two multiple linear regression models to compare their efficiency in the yielding predictions. One of the models used cultivation technology parameters as the inputs while the other used life factors as the inputs. Life factors were expressed in numeric values by using the following converter: total water consumption of the crop was used as the factor of water, the total sum of positive temperatures was used as the factor of heat, and the total sum of the main nutrients (NPK) available in the soil was used as the factor of nutrition. The results of the study proved an equal accuracy and reliability of the studied models of sweet corn yields, which is obvious from the values of RSQ. RSQ of the both studied regression models was 0.897. However, additional check of the modeling approaches applied in the feed-forward artificial neural network showed that the life factor based model with the RSQ value of 0.953 provided better yield predictions than the technologically based model with the RSQ value of 0.913. Therefore, we concluded that the life factor approach should be preferred to the technological approach in the development of yield predictive models for agriculture.
Źródło:
Journal of Ecological Engineering; 2019, 20, 6; 177-183
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Model for Assessing The Level of Automation of a Maintenance Department Using Artificial Neural Network
Autorzy:
Halikowski, Daniel
Patalas-Maliszewska, Justyna
Skrzeszewska, Małgorzata
Powiązania:
https://bibliotekanauki.pl/articles/118207.pdf
Data publikacji:
2018
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
maintenance departments
artificial neural network
manufacturing companies
działy utrzymania ruchu
sztuczna sieć neuronowa
firmy produkcyjne
Opis:
With regard to adapting enterprise to the Industry 4.0 concept, the first element should be the implementation and use of an information system within a manufacturing company. This article proposes a model, the use of which will allow the level of automation of a maintenance department to be forecast, depending on the effectivity of the use of the Manufacturing Executions System (MES) within a company. The model was built on the basis of the actual times of business processes completed which were supported by MES in the maintenance departments of two manufacturing enterprises using artificial neural network. As a result of research experiments, it was confirmed that the longer the time taken to complete business processes supported by MES, the higher is the degree of automation in a maintenance department.
Źródło:
Applied Computer Science; 2018, 14, 4; 70-80
1895-3735
Pojawia się w:
Applied Computer Science
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:
A novel approach for automatic detection and classification of suspicious lesions in breast ultrasound images
Autorzy:
Karimi, B.
Krzyżak, A.
Powiązania:
https://bibliotekanauki.pl/articles/91890.pdf
Data publikacji:
2013
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
automatic detection
classification
breast cancer
cancer lesions
ultrasound images
AdaBoost
artificial neural network
Fuzzy Support Vector Machine
Opis:
In this research, a new method for automatic detection and classification of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, de-noising using fuzzy logic and correlation among ultrasound images taken from different angles is used. Feature selection using combination of sequential backward search, sequential forward search and distance-based methods is obtained. A new segmentation method based on automatic selection of seed points and region growing is proposed and classification of lesions into two malignant and benign classes using combination of AdaBoost, Artificial Neural Network and Fuzzy Support Vector Machine classifiers and majority voting is implemented.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 4; 265-276
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
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:
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:
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:
A Small Wind Turbine Output Model for Spatially Constrained Remote Island Micro-Grids
Autorzy:
Žigman, D.
Meštrović, K.
Tomiša, T.
Powiązania:
https://bibliotekanauki.pl/articles/2172468.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
wind turbine
small wind turbine
decision tree model
artificial neural network model
random forest model
micro-grids
spatially constrained remote Island micro-grids
remote Island micro-grid
Opis:
Modelling operation of the power supply system for remote island communities is essential for its operation, as well as a survival of a modern society settled in challenging conditions. Micro-grid emerges as a proper solution for a sustainable development of a spatially constrained remote island community, while at the same time reflecting the power requirements of similar maritime subjects, such as large vessels and fleets. Here we present research results in predictive modelling the output of a small wind turbine, as a component of a remote island micro-grid. Based on a month-long experimental data and the machine learning-based predictive model development approach, three candidate models of a small wind turbine output were developed, and assessed on their performance based on an independent set of experimental data. The Random Forest Model out performed competitors (Decision Tree Model and Artificial Neural Network Model), emerging as a candidate methodology for the all-year predictive model development, as a later component of the over-all remote island micro-grid model.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2022, 16, 1; 143--146
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A wavelet-SARIMA-ANN hybrid model for precipitation forecasting
Hybrydowy model wavelet-SARIMA-ANN do prognozowania opadów
Autorzy:
Shafaei, M.
Adamowski, J.
Fakheri-Fard, M.
Dinpashoh, Y.
Adamowski, K.
Powiązania:
https://bibliotekanauki.pl/articles/292320.pdf
Data publikacji:
2016
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
artificial neural network (ANN)
precipitation forecasting
seasonal auto regressive integrated moving average (SARIMA)
water resources management
wavelet
gospodarka zasobami wodnymi
metoda wavelet
prognozowanie opadów
sezonowa zintegrowana autoregresja z ruchomą średnią
sztuczne sieci neuronowe
Opis:
Given its importance in water resources management, particularly in terms of minimizing flood or drought hazards, precipitation forecasting has seen a wide variety of approaches tested. As monthly precipitation time series have nonlinear features and multiple time scales, wavelet, seasonal auto regressive integrated moving average (SARIMA) and hybrid artificial neural network (ANN) methods were tested for their ability to accurately predict monthly precipitation. A 40-year (1970–2009) precipitation time series from Iran’s Nahavand meteorological station (34°12’N lat., 48°22’E long.) was decomposed into one low frequency subseries and several high frequency sub-series by wavelet transform. The low frequency sub-series were predicted with a SARIMA model, while high frequency subseries were predicted with an ANN. Finally, the predicted subseries were reconstructed to predict the precipitation of future single months. Comparing model-generated values with observed data, the wavelet-SARIMA-ANN model was seen to outperform wavelet-ANN and wavelet-SARIMA models in terms of precipitation forecasting accuracy.
Prognozowanie opadów, ze względu na ich znaczenie w gospodarce zasobami wodnymi, szczególnie w zmniejszaniu ryzyka powodzi czy susz, było już przedmiotem wielu badań. Serie miesięcznych opadów mają właściwości nieliniowe i różne skale czasowe, w związku z czym przetestowano różne metody: wavelet, metodę zintegrowanej sezonowej autoregresji z ruchomą średnią (SARIMA) i hybrydową metodę sztucznych sieci neuronowych (ANN) pod kątem ich zdolności do dokładnego przewidywania miesięcznych opadów. Czterdziestoletnią (1970–2009) serię opadów z irańskiej stacji meteorologicznej w Nahavand (34°12’N, 48°22’E) rozłożono na jedną podserię o niskiej częstotliwości i kilka podserii o wysokiej częstotliwości występowania opadów przez transformację falkową. Podserie o niskiej częstotliwości prognozowano za pomocą modelu SARIMA, podczas gdy podserie o wysokiej częstotliwości prognozowano, stosując ANN. Na koniec prognozowane podserie zrekonstruowano celem przewidywania opadów w poszczególnych miesiącach w przyszłości. Porównanie wartości generowanych przez model z danymi z obserwacji wykazało lepszą dokładność prognozowania opadów za pomocą modelu wavelet-SARIMA-ANN niż za pomocą modeli wavelet-ANN i wavelet-SARIMA.
Źródło:
Journal of Water and Land Development; 2016, 28; 27-36
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive prediction of stock exchange indices by state space wavelet networks
Autorzy:
Brdyś, M. A.
Borowa, A.
Idźkowiak, P.
Brdyś, M. T.
Powiązania:
https://bibliotekanauki.pl/articles/907656.pdf
Data publikacji:
2009
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
prognozowanie
giełda
sztuczna inteligencja
wyżarzanie symulowane
forecasting
stock exchange
artificial intelligence
state space wavelet network
simulated annealing
Opis:
The paper considers the forecasting of the Warsaw Stock Exchange price index WIG20 by applying a state space wavelet network model of the index price. The approach can be applied to the development of tools for predicting changes of other economic indicators, especially stock exchange indices. The paper presents a general state space wavelet network model and the underlying principles. The model is applied to produce one session ahead and five sessions ahead adaptive predictors of the WIG20 index prices. The predictors are validated based on real data records to produce promising results. The state space wavelet network model may also be used as a forecasting tool for a wide range of economic and non-economic indicators, such as goods and row materials prices, electricity/fuel consumption or currency exchange rates.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2009, 19, 2; 337-348
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations
Autorzy:
Brdyś, M. A.
Brdyś, M. T.
Maciejewski, S. M.
Powiązania:
https://bibliotekanauki.pl/articles/330138.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
currency exchange rate
artificial intelligence
state space wavelet network
Metropolis Monte Carlo
forecast combinations
data generating process
kurs wymiany walut
sztuczna inteligencja
sieć falkowa
Opis:
The paper considers the forecasting of the euro/Polish złoty (EUR/PLN) spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day-ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space wavelet network model is, in contrast to econometric forecast combinations, a non-parametric prediction technique which does not make any distributional assumptions regarding the underlying input variables. Both methods can be used as forecasting tools in portfolio investment management, asset valuation, IT security and integrated business risk intelligence in volatile market conditions.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2016, 26, 1; 161-173
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 393

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