文章基本信息

标题：Sequential Monte Carlo Methods for System Identification *
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作者：Thomas B. Schön ; Fredrik Lindsten ; Johan Dahlin 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：28
页码：775-786
DOI：10.1016/j.ifacol.2015.12.224
语种：English
出版社：Elsevier
摘要：AbstractOne of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two decades ago), provide numerical solutions to the nonlinear state estimation problems arising in SSMs. When combined with additional identification techniques, these algorithms provide solid solutions to the nonlinear system identification problem. We describe two general strategies for creating such combinations and discuss why SMC is a natural tool for implementing these strategies.
关键词：KeywordsNonlinear system identificationnonlinear state space modelparticle filterparticle smoothersequential Monte CarloMCMC