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Gradient-based iterative identification for Wiener nonlinear systems with non-uniform sampling

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Abstract

This paper focuses on the identification problem of Wiener nonlinear systems with non-uniform sampling. The mathematical model for the Wiener nonlinear system is established from the non-uniformly sampled input–output data. In order to solve the identification problem of the Wiener nonlinear system with the unmeasurable variables in the information vector, the gradient-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates. Finally, the simulation results indicate that the proposed algorithm is effective.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61273131), the Fundamental Research Funds for the Central Universities (JUDCF11042, JUDCF12031), and the PAPD of Jiangsu Higher Education Institutions and 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

    Lincheng Zhou, Xiangli Li & Feng Pan

  2. Jiangsu College of Information Technology, Wuxi, 214153, P.R. China

    Xiangli Li

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  1. Lincheng Zhou
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  2. Xiangli Li
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  3. Feng Pan
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Correspondence to Lincheng Zhou.

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Zhou, L., Li, X. & Pan, F. Gradient-based iterative identification for Wiener nonlinear systems with non-uniform sampling. Nonlinear Dyn 76, 627–634 (2014). https://doi.org/10.1007/s11071-013-1156-5

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  • DOI: https://doi.org/10.1007/s11071-013-1156-5

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