Neural Volterra filter for chaotic time series prediction
Xiao Xian-Cia, Li Heng-Chaob, Zhang Jia-Shub
a Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China; b Sichuan Province Key Laboratory of Signal and Information Processing,Southwest Jiaotong University, Chengdu 610031, 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
Published: 20 November 2005
(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:
Xiao Xian-Ci, Li Heng-Chao, Zhang Jia-Shu Neural Volterra filter for chaotic time series prediction 2005 Chin. Phys. 14 2181