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Auxiliary Model-Based Forgetting Factor Stochastic Gradient Algorithm for Dual-Rate Nonlinear Systems and its Application to a Nonlinear Analog Circuit

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Abstract

This paper studies the identification problem of dual-rate Hammerstein nonlinear systems. By means of the key-term separation principle, we develop a regression identification model with different input and output sampling rates. In order to promote the convergence rate of the stochastic gradient (SG) algorithm, an auxiliary model-based forgetting factor SG algorithm is derived. Finally, the proposed algorithm is applied to model a nonlinear analog circuit with dual-rate sampling and the simulation result shows the effectiveness of the algorithm.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China, the Fundamental Research Funds for the Central Universities (JUDCF11042, JUDCF12031), 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, People’s Republic of China

    **angli Li & Lincheng Zhou

  2. Jiangsu College of Information Technology, Wuxi,  214153, People’s Republic of China

    **angli Li

  3. School of Internet of Things Engineering, Jiangnan University, Wuxi,  214122, People’s Republic of China

    Ruifeng Ding

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  1. **angli Li
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  2. Lincheng Zhou
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  3. Ruifeng Ding
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Correspondence to **angli Li.

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Li, X., Zhou, L. & Ding, R. Auxiliary Model-Based Forgetting Factor Stochastic Gradient Algorithm for Dual-Rate Nonlinear Systems and its Application to a Nonlinear Analog Circuit. Circuits Syst Signal Process 33, 1957–1969 (2014). https://doi.org/10.1007/s00034-013-9733-x

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  • DOI: https://doi.org/10.1007/s00034-013-9733-x

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