文章基本信息

标题：Nonlinear FIR Identification with Model Order Reduction Steiglitz-McBride ⁎
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作者：Mina Ferizbegovic ; Miguel Galrinho ; Håkan Hjalmarsson 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：646-651
DOI：10.1016/j.ifacol.2018.09.218
语种：English
出版社：Elsevier
摘要：AbstractIn system identification, many structures and approaches have been proposed to deal with systems with non-linear behavior. When applicable, the prediction error method, analogously to the linear case, requires minimizing a cost function that is non-convex in general. The issue with non-convexity is more problematic for non-linear models, not only due to the increased complexity of the model, but also because methods to provide consistent initialization points may not be available for many model structures. In this paper, we consider a non-linear rational finite impulse response model. We observe how the prediction error method requires minimizing a non-convex cost function, and propose a three-step least-squares algorithm as an alternative procedure. This procedure is an extension of the Model Order Reduction Steiglitz-McBride method, which is asymptotically efficient in open loop for linear models. We perform a simulation study to illustrate the applicability and performance of the method, which suggests that it is asymptotically efficient.
关键词：KeywordsSystem identificationnon-linearleast-squares identification