Multi-layered Bayesian Neural Networks for Simulation and Prediction Stress-Strain Time Series
DOI:
https://doi.org/10.26636/jtit.2015.3.967Keywords:
Bayesian Neural Networks, Kalman filteringAbstract
The aim of the paper is to investigate the differences as far as the numerical accuracy is concerned between feed-forward 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.
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