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

Authors

  • Agnieszka Krok Cracow University of Technology, Institute of Computer Methods in Civil Engineering
    Poland

Keywords:

Artificial Neural Networks (ANN), Kalman Filter (KF), Node Decoupled Extended Kalman Filtering (NDEKF), Multilayer Perceptron (MPL), Genetic Algorithm (AG), Bayesian methods, concrete specimens, cyclic loading, hysteresis loops

Abstract

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.

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Published

2017-01-25

Issue

Vol. 18 No. 4 (2011)
pp. 275–282

Section

Articles

License

Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN).

How to Cite

Krok, A. (2017). An improved Neural Kalman Filtering Algorithm in the analysis of cyclic behaviour of concrete specimens. Computer Assisted Methods in Engineering and Science, 18(4), 275–282. https://cames3.ippt.pan.pl/index.php/cames/article/view/105