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Multi-innovation Extended Stochastic Gradient Algorithm and Its Performance Analysis

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Abstract

This paper derives the multi-innovation extended stochastic gradient algorithm for controlled autoregressive moving average models by expanding the scalar innovation to an innovation vector and analyzes its performance in detail. Four convergence theorems are given for the multi-innovation extended stochastic gradient algorithm to show that the parameter estimates converge to their true values under the weak persistent excitation condition. The simulation results show that the proposed algorithm can produce more accurate parameter estimates than the traditional extended stochastic gradient algorithm.

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Authors and Affiliations

  1. School of Communication and Control Engineering, Jiangnan University, Wuxi, 214122, P.R. China

    Yanjun Liu, Li Yu & Feng Ding

Authors
  1. Yanjun Liu
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  2. Li Yu
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  3. Feng Ding
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Corresponding author

Correspondence to Feng Ding.

Additional information

This work was supported in part by the National Natural Science Foundation of China (60973043), the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars (State Education Ministry) and the Program for Innovative Research Team of Jiangnan University.

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Liu, Y., Yu, L. & Ding, F. Multi-innovation Extended Stochastic Gradient Algorithm and Its Performance Analysis. Circuits Syst Signal Process 29, 649–667 (2010). https://doi.org/10.1007/s00034-010-9174-8

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  • DOI: https://doi.org/10.1007/s00034-010-9174-8

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