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Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems

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Abstract

We consider the parameter estimation problem for Hammerstein finite impulse response (FIR) systems. An estimated noise transfer function is used to filter the input–output data of the Hammerstein system. By combining the key-term separation principle and the filtering theory, a recursive least squares algorithm and a filtering-based recursive least squares algorithm are presented. The proposed filtering-based recursive least squares algorithm can estimate the noise and system models. The given examples confirm that the proposed algorithm can generate more accurate parameter estimates and has a higher computational efficiency than the recursive least squares algorithm.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 601174032), the Doctoral Foundation of Higher Education Priority Fields of Scientific Research (No. 20110093130001) and in part by the 111 Project (B12018).

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, P.R. China

    Ziyun Wang, Yanxia Shen & Zhicheng Ji

  2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, P.R. China

    Rui Ding

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  1. Ziyun Wang
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Correspondence to Ziyun Wang.

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Wang, Z., Shen, Y., Ji, Z. et al. Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems. Nonlinear Dyn 73, 1045–1054 (2013). https://doi.org/10.1007/s11071-013-0851-6

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