Blind Identification of Wiener Models^*

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• 作者：Adrian Wills^* ; ^{Adrian.Wills@newcastle.edu.au" class="auth_mail" title="E-mail the corresponding author} ; Thomas B. Schö ; n^** ; ^{schon@isy.liu.se" class="auth_mail" title="E-mail the corresponding author} ; Lennart Ljung^** ; ^{ljung@isy.liu.se" class="auth_mail" title="E-mail the corresponding author} ; Brett Ninness^* ; ^{Brett.Ninness@newcastle.edu.au" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Wiener Model ; Nonlinear Systems ; Maximum Likelihood ; System Identification ; Parameter Estimation
• 刊名：IFAC-PapersOnLine
• 出版年：2011
• 出版时间：January 2011
• 年：2011
• 卷：44
• 期：1
• 页码：5597-5602
• 全文大小：913 K

文摘

This paper develops and illustrates methods for the identification of Wiener model structures. These techniques are capable of accommodating the “blind” situation where the input excitation to the linear block is not observed. Furthermore, the algorithm developed here can accommodate a nonlinearity which need not be invertible, and may also be multivariable. Central to these developments is the employment of the Expectation Maximisation (EM) method for computing maximum likelihood estimates, and the use of a new approach to particle smoothing to efficiently compute stochastic expectations in the presence of nonlinearities.