An improved Neural Kalman Filtering Algorithm in the analysis of cyclic behaviour of concrete specimens

• Autorzy: Krok A.
• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

artificial neural networks; Kalman filtering; multilayer perceptron; genetic algorithm; Bayesian method; concrete specimens; cyclic loading; hysteresis loops

PL

sztuczne sieci neuronowe; filtracja Kalmana; metoda bayesowska; algorytm genetyczny; ładowanie cykliczne; pętle histerezy

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2011
• Tom: Vol. 18, no. 4
• Strony: 275--282
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Krok A.

• Cracow University of Technology Institute of Computer Methods in Civil Engineering Warszawska 24, 31-155 Cracov, Poland,

Bibliografia

• [1] S. Haykin. Neural Networks, A Comprehensive Foundation, 2nd Ed. MacMillan College Publ., Engle-wood Cliffs, NJ, 1999.
• [2] S. Haykin. [Ed.], Kalman Filtering and Neural Networks, John Wiley & Sons, New York, 2001.
• [3] R.E. Kalman. A new approach to linear filtering and prediction problems. Transactions of ASME, Journal of Basic Engineering, 82(D): 35–45, 1960.
• [4] A. Krok, Z.Waszczyszyn. Neural prediction of response spectra from mining tremors using recurrent layered networks and Kalman filtering. In: J-K.Bathe [Ed.] Proc. 3rd MIT Conf. Computational Fluid and Solid Mechanics, pp. 302–305. Elsevier, 2005.
• [5] A. Krok, Z. Waszczyszyn. Simulation of building loops for a superconductor using neural networks with Kalman filtering. Computer Assisted Mechanics and Engineering Sciences, 13: 575–582, 2006.
• [6] A.Krok, Z. Waszczyszyn. Kalman filtering for neural prediction of response spectra from mining tremors. Computers and Structures, 85(15–16): 1257–1263, 2007.
• [7] A. Krok. Analysis of Mechanics of Structures and Material Problems Applying Artificial Neural Networks Learnt by Means of Kalman Fltering (in Polish), Ph.D. Thesis, Institute of Computer Methods in Civil Engineering. Cracow Univ. of Technology, 2007.
• [8] D. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge Univ. Press, 2003.
• [9] I.T. Nabney. Netlab, Algorithms for pattern recognition. Springer, 2006.
• [10] L. Prechelt. Adaptive Parameter Pruning in Neural Networks. International Computer Science Institute University of California Technical Report TR-95-009, 1995.
• [11] B.P. Sinha, K. H. Gerstle, L. G. Tulin, Stress-strain relations for concrete under cyclic loading. Journal of American Concrete Inst., 61(12): 1964.
• [12] Neural Network Toolbox for Use with MATLAB, User’s Guide, Version 4. The MathWorks, Inc., Natick, MA, 2000.