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Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique

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Abstract

This paper presents two estimation algorithms for Hammerstein controlled autoregressive autoregressive systems. The key-term separation principle is used to solve the problem that the identification model contains the products of the parameters of the nonlinear part and the linear part, which causes large amount of computation. To improve the parameter estimation accuracy of the stochastic gradient algorithm, we derive a forgetting factor multi-innovation generalized stochastic gradient algorithm expanding the innovation length. To improve the convergence rate, we derive a filtering-based forgetting factor multi-innovation stochastic gradient algorithm using the filtering technique. The simulation results show that the proposed algorithms are effective.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61273194) and the PAPD of Jiangsu Higher Education Institutions.

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Authors and Affiliations

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

    Yawen Mao & Feng Ding

  2. Control Science and Engineering Research Center, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Feng Ding

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  1. Yawen Mao
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  2. Feng Ding
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Correspondence to Feng Ding.

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Mao, Y., Ding, F. Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique. Nonlinear Dyn 79, 1745–1755 (2015). https://doi.org/10.1007/s11071-014-1771-9

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  • DOI: https://doi.org/10.1007/s11071-014-1771-9

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