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Proceedings of the 18th IFAC World Congress

DOI: 10.3182/20110828-6-it-1002.02610

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Blind Identification of Wiener Models

Journal article published in 2011 by Adrian Wills, Thomas B. Schön ORCID, Lennart Ljung, Brett Ninness
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

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.