Identification of hysteretic systems using the differential evolution algorithm

Abstract

A widely used model in the field of hysteretic or memory-dependent vibrations is that of Bouc and Wen. Different parameter values extend its use to various areas of mechanical vibrations. As a consequence an identification method is required to identify the parameter values relevant to its application. Its structure, however, includes internal states and non-linear terms. This rules out the conventional identification methods, such as least squares and maximum likelihood because they require derivative calculations of the prediction error with respect to the parameters. In this paper are presented some results for Bouc-Wen model identification, using simulated noise-free data, simulated noisy data and experimental data obtained from a nuclear power plant. The method used to achieve this is the differential evolution algorithm. Differential evolution (DE) is an optimization method developed to perform direct search in a continuous parameter space without requiring any derivative estimation.