Please wait a minute...
Chin. Phys., 2005, Vol. 14(11): 2181-2188    DOI: 10.1088/1009-1963/14/11/007
GENERAL Prev   Next  

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.
Keywords:  chaotic time series      adaptive neural Volterra filter      conjugate gradient algorithm  
Received:  31 March 2005      Revised:  11 May 2005      Published:  20 November 2005
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: 

Xiao Xian-Ci, Li Heng-Chao, Zhang Jia-Shu Neural Volterra filter for chaotic time series prediction 2005 Chin. Phys. 14 2181

[1] New predication of chaotic time series based on local Lyapunov exponent
Zhang Yong. Chin. Phys. B, 2013, 22(5): 050502.
[2] A method to improve the precision of chaotic time series prediction by using a non-trajectory
Yan Hua, Wei Ping, Xiao Xian-Ci. Chin. Phys. B, 2009, 18(8): 3287-3294.
[3] Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network
Ma Qian-Li, Zheng Qi-Lun, Peng Hong, Zhong Tan-Wei, Qin Jiang-Wei. Chin. Phys. B, 2008, 17(2): 536-542.
[4] Chaotic time series prediction using fuzzy sigmoid kernel-based support vector machines
Liu Han, Liu Ding, Deng Ling-Feng. Chin. Phys. B, 2006, 15(6): 1196-1200.
[5] Chaotic time series prediction using mean-field theory for support vector machine    
Cui Wan-Zhao, Zhu Chang-Chun, Bao Wen-Xing, Liu Jun-Hua. Chin. Phys. B, 2005, 14(5): 922-929.
[6] Local discrete cosine transformation domain Volterra prediction of chaotic time series
Xiao Xian-Ci, Zhang Jia-Shu, Li Heng-Chao. Chin. Phys. B, 2005, 14(1): 49-54.
[7] Chaotic time series prediction using least squares support vector machines
Ye Mei-Ying, Wang Xiao-Dong. Chin. Phys. B, 2004, 13(4): 454-458.
[8] Improving the prediction of chaotic time series
Chen Tian-Lun, Li Ke-Ping, Gao Zi-You. Chin. Phys. B, 2003, 12(11): 1213-1217.
Zhang Jia-shu, Xiao Xian-ci. Chin. Phys. B, 2001, 10(5): 390-394.
No Suggested Reading articles found!