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Markov chain approach to identifying Wiener systems

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Abstract

Identification of the Wiener system composed of an infinite impulse response (IIR) linear subsystem followed by a static nonlinearity is considered. The recursive estimates for unknown coefficients of the linear subsystem and for the values of the nonlinear function at any fixed points are given by the stochastic approximation algorithms with expanding truncations (SAAWET). With the help of properties of the Markov chain connected with the linear subsystem, all estimates derived in the paper are proved to be strongly consistent. In comparison with the existing results on the topic, the method presented in the paper simplifies the convergence analysis and requires weaker conditions. A numerical example is given, and the simulation results are consistent with the theoretical analysis.

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Authors and Affiliations

  1. Key Laboratory of Systems and Control, Institute of Systems Science, AMSS, Chinese Academy of Sciences, Beijing, 100190, China

    WenXiao Zhao & HanFu Chen

  2. National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China

    WenXiao Zhao & HanFu Chen

Authors
  1. WenXiao Zhao
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  2. HanFu Chen
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Correspondence to HanFu Chen.

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Zhao, W., Chen, H. Markov chain approach to identifying Wiener systems. Sci. China Inf. Sci. 55, 1201–1217 (2012). https://doi.org/10.1007/s11432-012-4582-y

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  • DOI: https://doi.org/10.1007/s11432-012-4582-y

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