The Use of Genetic Algorithms for Searching Parameter Space in Gaussian Process Modeling

Abstract

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.