文章基本信息

标题：Identification of a Class of Nonlinear Dynamical Networks ⁎
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作者：Mohamed R.H. Abdalmoaty ; Cristian R. Rojas ; 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：868-873
DOI：10.1016/j.ifacol.2018.09.113
语种：English
出版社：Elsevier
摘要：AbstractIdentification of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, twostochasticlinear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.
关键词：KeywordsSystem IdentificationDynamical NetworksStochastic SystemsBlock-Oriented ModelsPrediction Error Method