文章基本信息

标题：A Robust Algorithm for Online Switched System Identification ⁎
本地全文：下载
作者：Zhe Du ; Laura Balzano ; Necmiye Ozay 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：293-298
DOI：10.1016/j.ifacol.2018.09.150
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, we consider the problem of online identification of Switched Au-toRegressive eXogenous (SARX) systems, where the goal is to estimate the parameters of each subsystem and identify the switching sequence as data are obtained in a streaming fashion. We propose a two-step algorithm: (i) every time we receive new data, we first assign this data to one candidate subsystem based on a novel robust criterion that incorporates both the residual error and an upper bound of subsystem estimation error, and (ii) we use a randomized algorithm to update the parameter estimate of chosen candidate. We provide a theoretical guarantee on the local convergence of our algorithm. Though our theory only guarantees convergence with a good initialization, simulation results show that even with random initialization, our algorithm still has excellent performance. Finally, we show, through simulations, that our algorithm outperforms existing methods and exhibits robust performance.
关键词：KeywordsSystem identificationOnline identification algorithmConvergence analysis