Prediction for chaotic time series based on discrete volterra neural networks

Authors:
Li-Sheng Yin;Xi-Yue Huang;Zu-Yuan Yang;Chang-Cheng Xiang
Affiliations:
College of Automation, University of Chongqing, Chongqing, China;College of Automation, University of Chongqing, Chongqing, China;College of Automation, University of Chongqing, Chongqing, China;College of Automation, University of Chongqing, Chongqing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

In this paper, based on the Volterra expansion of nonlinear dynamical system functions and the deterministic and nonlinear characterization of chaotic time series, the discrete Volterra neural networks are proposed to make prediction of chaotic time series. The predictive model of chaotic time series is established with the discrete Volterra neural networks and the steps of the learning algorithm with discrete Volterra neural networks are expressed. The predictive model and the learning algorithm are more effective and reliable than the adaptive higher-order nonlinear FIR filter. The Experimental and simulating results show the discrete Volterra neural networks can be successfully used to predict chaotic time series.