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Weighted hierarchical stochastic gradient identification algorithms for ARX models

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  • Rui-Qi Dong
  • Ying Zhang
  • Ai-Guo Wu

Abstract

In this paper, a weighted hierarchical stochastic gradient algorithm and a latest estimation-based weighted hierarchical stochastic gradient algorithm for ARX models are proposed. Different from some existing stochastic gradient algorithms, the correction term of the developed algorithms is in a weighted form of the correction terms in the current and last recursive steps of the hierarchical stochastic gradient algorithm. Further, the convergence property of the presented latest estimation-based weighted hierarchical stochastic gradient algorithm is analysed. It is illustrated by a numerical example that both the weighted hierarchical stochastic gradient and the latest estimation-based weighted hierarchical stochastic gradient algorithms possess higher convergence accuracy compared with some existing hierarchical stochastic gradient algorithms if the weighting factor is appropriately chosen.

Suggested Citation

  • Rui-Qi Dong & Ying Zhang & Ai-Guo Wu, 2021. "Weighted hierarchical stochastic gradient identification algorithms for ARX models," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(2), pages 363-373, January.
    • Handle: RePEc:taf:tsysxx:v:52:y:2021:i:2:p:363-373
    DOI: 10.1080/00207721.2020.1829163
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