Identification of hysteretic systems using NARX models, part II:a Bayesian approach

Following on from the first part of this short sequence, this paper will investigate the use of a Bayesian methodology for the identification of Bouc-Wen hysteretic systems by NARX models. The approach - based on Markov Chain Monte Carlo - offers a number of advantages over the evolutionary approach of the first paper. Among them are the ability to sample from the probability density functions of the parameters in order to develop nonparametric estimators and the possibility of selecting model terms in a principled manner. The paper will investigate the use of the Deviance Information Criterion (DIC) as a means of selecting model terms, specifically the special basis functions developed for the Bouc-Wen system in Part I. Results for simulated data will be given