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A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network

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  • Qinghai Li
  • Rui-Chang Lin

Abstract

A self-constructing fuzzy neural network (SCFNN) has been successfully used for chaotic time series prediction in the literature. In this paper, we propose the strategy of adding a recurrent path in each node of the hidden layer of SCFNN, resulting in a self-constructing recurrent fuzzy neural network (SCRFNN). This novel network does not increase complexity in fuzzy inference or learning process. Specifically, the structure learning is based on partition of the input space, and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. This novel network can also be applied for chaotic time series prediction including Logistic and Henon time series. More significantly, it features rapider convergence and higher prediction accuracy.

Suggested Citation

  • Qinghai Li & Rui-Chang Lin, 2016. "A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, December.
    • Handle: RePEc:hin:jnlmpe:3542898
    DOI: 10.1155/2016/3542898
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    Cited by:

    1. Miao, Hua & Zhu, Wei & Dan, Yuanhong & Yu, Nanxiang, 2024. "Chaotic time series prediction based on multi-scale attention in a multi-agent environment," Chaos, Solitons & Fractals, Elsevier, vol. 183(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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