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

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Authors

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

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.

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