文章基本信息

标题：Nonparametric models for Hammerstein-Wiener and Wiener-Hammerstein system identification
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作者：Riccardo S. Risuleo ; Håkan Hjalmarsson
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：400-405
DOI：10.1016/j.ifacol.2020.12.198
语种：English
出版社：Elsevier
摘要：AbstractWe propose a framework for modeling structured nonlinear systems using nonpara-metric Gaussian processes. In particular, we introduce a two-layer stochastic model of latent interconnected Gaussian processes suitable for modeling Hammerstein-Wiener and Wiener-Hammerstein cascades. The posterior distribution of the latent processes is intractable because of the nonlinear interactions in the model; hence, we propose a Markov Chain Monte Carlo method consisting of a Gibbs sampler where each step is implemented using elliptical-slice sampling. We present the results on two example nonlinear systems showing that they can effectively be modeled and identified using the proposed nonparametric modeling approach.
关键词：KeywordsNonlinear system identificationBayesian methodsNonparametric methods