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
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
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.
Received: 31 March 2005
Revised: 11 May 2005
Accepted manuscript online:
PACS:
05.45.Tp
(Time series analysis)
Fund: Project supported by the National Natural Science Foundation of China (Grant No 60276096), the National Ministry Foundation of China (Grant No 51430804QT2201).
Cite this article:
Li Heng-Chao (李恒超), Zhang Jia-Shu (张家树), Xiao Xian-Ci (肖先赐) Neural Volterra filter for chaotic time series prediction 2005 Chinese Physics 14 2181
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