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Auxiliary Model-Based Iterative Estimation Algorithms for Nonlinear Systems Using the Covariance Matrix Adaptation Strategy

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Abstract

This paper focuses on the parameter estimation problem of Hammerstein nonlinear systems with colored noise. By virtue of the covariance matrix adaptation strategy and the auxiliary model identification idea, an auxiliary model-based covariance matrix adaptation (AM-CMA) identification algorithm is presented. Furthermore, for the purpose of improving the optimization efficiency of the AM-CMA algorithm, we introduce the gradient search into the AM-CMA algorithm and propose an auxiliary model-based covariance matrix and gradient search adaptation (AM-CMGA) identification algorithm. The proposed algorithms are efficient and can give satisfactory parameter estimation results. The proposed algorithms are efficient and can give satisfactory parameter estimation results. The simulation examples are provided to demonstrate the effectiveness of our approaches.

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Acknowledgements

This work was supported in part by the research project of National Natural Science Foundations of China (Nos. 62103167 and 12102146), the Natural Science Foundations of Jiangsu Province (Nos. BK20210451 and BK20200611), and in part by the research project of Jiangnan University (JUSRP12028 and JUSRP12040).

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  1. School of Science, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Yawen Mao, Jing Chen, Yan Pu & Qingyuan Hu

  2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, People’s Republic of China

    Chen Xu

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  1. Yawen Mao
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Mao, Y., Xu, C., Chen, J. et al. Auxiliary Model-Based Iterative Estimation Algorithms for Nonlinear Systems Using the Covariance Matrix Adaptation Strategy. Circuits Syst Signal Process 41, 6750–6773 (2022). https://doi.org/10.1007/s00034-022-02112-5

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