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Chaos time-series prediction based on an improved recursive Levenberg–Marquardt algorithm

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  • Shi, Xiancheng
  • Feng, Yucheng
  • Zeng, Jinsong
  • Chen, Kefu

Abstract

An improved recursive Levenberg–Marquardt algorithm (RLM) is proposed to more efficiently train neural networks. The error criterion of the RLM algorithm was modified to reduce the impact of the forgetting factor on the convergence of the algorithm. The remedy to apply the matrix inversion lemma in the RLM algorithm was extended from one row to multiple rows to improve the success rate of the convergence; after that, the adjustment strategy was modified based on the extended remedy. Finally, the performance of this algorithm was tested on two chaotic systems. The results show improved convergence.

Suggested Citation

  • Shi, Xiancheng & Feng, Yucheng & Zeng, Jinsong & Chen, Kefu, 2017. "Chaos time-series prediction based on an improved recursive Levenberg–Marquardt algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 100(C), pages 57-61.
    • Handle: RePEc:eee:chsofr:v:100:y:2017:i:c:p:57-61
    DOI: 10.1016/j.chaos.2017.04.032
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    References listed on IDEAS

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    1. Pan, Shing-Tai & Lai, Chih-Chin, 2008. "Identification of chaotic systems by neural network with hybrid learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 233-244.
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    Cited by:

    1. Sangiorgio, Matteo & Dercole, Fabio, 2020. "Robustness of LSTM neural networks for multi-step forecasting of chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Sangiorgio, Matteo & Dercole, Fabio & Guariso, Giorgio, 2021. "Forecasting of noisy chaotic systems with deep neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).

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