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1
Content available The Use of Genetic Algorithms for Searching Parameter Space in Gaussian Process Modeling
Krok A.
Journal of Telecommunications and Information Technology
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2015
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nr 3
58--63
EN
The aim of the paper is to present the possibilities of modeling the experimental data by Gaussian processes. Genetic algorithms are used for finding the Gaussian process parameters. Comparison of data modeling accuracy is made according to neural networks learned by Kalman filtering. Concrete hysteresis loops obtained by the experiment of cyclic loading are considered as the real data time series.
2
Content available Multi-layered Bayesian Neural Networks for Simulation and Prediction Stress-Strain Time Series
Krok A.
Journal of Telecommunications and Information Technology
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2015
|
nr 3
45--51
EN
The aim of the paper is to investigate the differences as far as the numerical accuracy is concerned between feedforward layered Artificial Neural Networks (ANN) learned by means of Kalman filtering (KF) and ANN learned by means of the evidence procedure for Bayesian technique. The stress-strain experimental time series for concrete hysteresis loops obtained by the experiment of cyclic loading is presented as considered example.
3
Content available The development of Kalman Filter learning technique for Artificial Neural Networks
Krok A.
Journal of Telecommunications and Information Technology
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2013
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nr 4
16--21
EN
The paper presents an idea of using the Kalman Filtering (KF) for learning the Artificial Neural Networks (ANN). It is shown that KF can be fully competitive or more beneficial method with comparison standard Artificial Neural Networks learning techniques. The development of the method is presented respecting selective learning of chosen part of ANN. Another issue presented in this paper is the author’s concept of automatic selection of architecture of ANN learned by means of KF based on removing unnecessary connection inside the network. The effectiveness of presented ideas is illustrated on the examples of time series modeling and prediction. Considered data came from the experiments and situ measurements in the field of structural mechanics and materials.
4
Content available remote An improved Neural Kalman Filtering Algorithm in the analysis of cyclic behaviour of concrete specimens
Krok A.
Computer Assisted Mechanics and Engineering Sciences
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2011
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Vol. 18, no. 4
275-282
EN
The article is related to the results of research on Node Decoupled Extended Kalman Filtering (NDEKF) as a learning method for the training of Multilayer Perceptron (MPL). Developments of this method made by the author are presented. The application of NDEKF and MPL and other methods (pruning of MLP, Gauss Process model calibrated by Genetic Algorithm and Bayesian learning methods) are discussed on the problem of hysteresis loop simulations for tests of compressed concrete specimens subjected to cyclic loading.
5
Content available remote Symulacja pętli histerezy betonu z wykorzystaniem sztucznych sieci neuronowych i metod Bayesowskich
Krok A.
Zeszyty Naukowe Politechniki Rzeszowskiej. Mechanika
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2008
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z. 74 [258]
165-173
EN
Feed-forward layered Artificial Neural Networks (ANN) learnt by means the evidence procedure for Bayesian technique are used for simulation and prediction of hysteresis loops. Concrete hysteresis loops obtained by cyclic loading are considered. ANN were learned and tested on the experimental data. The prediction of the stress - strain relation was made for the last part of the experiment, basing on its previous stage.
6
Zastosowanie sztucznych sieci neuronowych i filtru Kalmana do modelowania pętli histerezy od obciążeń niskocyklicznych próbek betonowych
Krok A.
Zeszyty Naukowe. Budownictwo / Politechnika Śląska
|
2006
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z. 109
163-170
PL
Filtr Kalmana, jako metoda uczenia Sztucznych Sieci Neuronowych, została wykorzystana w celu modelowania prób zniszczenia niskocyklowego typu rozciąganie-rozciąganie" oraz predykcji zachowania się materiału pod wpływem zadanego obciążenia.
EN
Kalman filtering is used as a learning method for the training of Feedforward Layered Neural Networks (FLNN). These networks were applied to the simulation of hysteresis loops obtained by the experiment of cyclic loading of concrete specimens. Prediction of material behavior under given loading is also dane.
7
Content available remote Simulation of hysteresis loops for a superconductor using neural networks with Kalman filtering
Krok A.
Computer Assisted Mechanics and Engineering Sciences
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2006
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Vol. 13, No. 4
575-582
EN
Kalman filtering is used as a learning method for the training of Feed-forward Layered Neural Networks (FLNN) and Recurrent LNNs (RLNN). These networks were applied to the simulation of hysteresis loops obtained by the experiment on a cable-in-conduit superconductors by the test carried out in a cryogenic press [8]. The training and testing patterns were taken from nine selected, characteristic hysteresis loops. The formulated FLNN: 4-4-5-1 gives the computer simulation of higher accuracy than the standard network FLNN: 4-7-5-1 discussed in [5].
8
Content available remote Zastosowanie sieci neuronowej i filtru Kalmana do symulacji spektrów odpowiedzi budynków poddanych działaniu wymuszeń parasejsmicznych
Krok A., Waszczyszyn Z.
Zeszyty Naukowe Politechniki Rzeszowskiej. Mechanika
|
2005
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z. 65 [222]
219-226
EN
Decoupled Extended Kalman Filter (DEKF) algorithm was used for the training of Feed-forward Layer Neural Network (FLNN). Data taken from [1] correspond to Displacement Response Spectra (DRS) computed on the base of vibration records measured on the ground level for paraseismic excitation (inputs to FLNN) and on the fourth floor of monitored buildings (outputs of FLNN). It was proved that the application of DEKF gives much more accurate predictions of DRS than standard NN discussed in [1].
9
Content available remote Sposoby odwadniania kopalń kruszcowych w rejonie Olkusza w XV i XVI w.
Krok A.
Pompy, Pompownie
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2001
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nr 4 (102)
49-50
10
Content available remote Przygotowanie pompy do rozruchu
Korczak A., Kocjan R., Krok A., Grabowski M.
Pompy, Pompownie
|
1999
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nr 12 (86)
11-14
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