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Filtering Based Recursive Least Squares Algorithm for Multi-Input Multioutput Hammerstein Models

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  • Ziyun Wang
  • Yan Wang
  • Zhicheng Ji

Abstract

This paper considers the parameter estimation problem for Hammerstein multi-input multioutput finite impulse response (FIR-MA) systems. Filtered by the noise transfer function, the FIR-MA model is transformed into a controlled autoregressive model. The key-term variable separation principle is used to derive a data filtering based recursive least squares algorithm. The numerical examples confirm that the proposed algorithm can estimate parameters more accurately and has a higher computational efficiency compared with the recursive least squares algorithm.

Suggested Citation

  • Ziyun Wang & Yan Wang & Zhicheng Ji, 2014. "Filtering Based Recursive Least Squares Algorithm for Multi-Input Multioutput Hammerstein Models," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, October.
    • Handle: RePEc:hin:jnlmpe:232848
    DOI: 10.1155/2014/232848
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