文章基本信息

标题：Identification of Wiener Models in the Presence of Structural Disturbances
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作者：Amir H. Shirdel ; Jari M. Böling ; Hannu T. Toivonen 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：14094-14099
DOI：10.1016/j.ifacol.2017.08.1849
语种：English
出版社：Elsevier
摘要：AbstractIn empirical system identification, non-stationary structural disturbances, such as trends and outliers, can have a negative effect on the estimation of the system parameters. As it not possible to determine a priori which parts of the measured data stem from structural disturbances and which are due to the system dynamics, the identification of structural disturbances and system model should be done simultaneously. In this study, a method for output error identification of nonlinear Wiener models in the case when the measurement is affected by trends and outliers is presented. The Wiener model can be described by a dynamic linear block followed by an static nonlinear block. In the proposed method the dynamic block is expanded using orthonormal basis functions, while the static nonlinear block is modeled by a kernel model. The kernel parameters and structural disturbances are estimated simultaneously by using sparse optimization, which is solved usingl1-regularization and iterative reweighting. The feasibility of the proposed method is demonstrated on a simulated example.
关键词：KeywordsSystem IdentificationTrend filteringl1-regularizationSparse optimizationOrthonormal basis functionsNonlinear systemWiener modelRadial basis functions