中国物理B ›› 2005, Vol. 14 ›› Issue (11): 2181-2188.doi: 10.1088/1009-1963/14/11/007

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Neural Volterra filter for chaotic time series prediction

肖先赐1, 李恒超2, 张家树2   

  1. (1)Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China; (2)Sichuan Province Key Laboratory of Signal and Information Processing,Southwest Jiaotong University, Chengdu 610031, China
  • 收稿日期:2005-03-31 修回日期:2005-05-11 出版日期:2005-11-20 发布日期:2005-11-20
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No 60276096), the National Ministry Foundation of China (Grant No 51430804QT2201).

Neural Volterra filter for chaotic time series prediction

Li Heng-Chao (李恒超)a, Zhang Jia-Shu (张家树)a, Xiao Xian-Ci (肖先赐)b    

  1. a Sichuan Province Key Laboratory of Signal and Information Processing,Southwest Jiaotong University, Chengdu 610031, China; b Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
  • Received:2005-03-31 Revised:2005-05-11 Online:2005-11-20 Published:2005-11-20
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No 60276096), the National Ministry Foundation of China (Grant No 51430804QT2201).

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摘要： A new second-order neural Volterra filter (SONVF) with conjugate gradient (CG) algorithm is proposed to predict chaotic time series based on phase space delay-coordinate reconstruction of chaotic dynamics system in this paper, where the neuron activation functions are introduced to constraint Volterra series terms for improving the nonlinear approximation of second-order Volterra filter (SOVF). The SONVF with CG algorithm improves the accuracy of prediction without increasing the computation complexity. Meanwhile, the difficulty of neuron number determination does not exist here. Experimental results show that the proposed filter can predict chaotic time series effectively, and one-step and multi-step prediction performances are obviously superior to those of SOVF, which demonstrate that the proposed SONVF is feasible and effective.

关键词: chaotic time series, adaptive neural Volterra filter, conjugate gradient algorithm

Abstract: A new second-order neural Volterra filter (SONVF) with conjugate gradient (CG) algorithm is proposed to predict chaotic time series based on phase space delay-coordinate reconstruction of chaotic dynamics system in this paper, where the neuron activation functions are introduced to constraint Volterra series terms for improving the nonlinear approximation of second-order Volterra filter (SOVF). The SONVF with CG algorithm improves the accuracy of prediction without increasing the computation complexity. Meanwhile, the difficulty of neuron number determination does not exist here. Experimental results show that the proposed filter can predict chaotic time series effectively, and one-step and multi-step prediction performances are obviously superior to those of SOVF, which demonstrate that the proposed SONVF is feasible and effective.

Key words: chaotic time series, adaptive neural Volterra filter, conjugate gradient algorithm

中图分类号:  (Time series analysis)

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