School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, China

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Abstract—With the analysis of the technology of phase space reconstruction, a modeling and forecasting technique based on the Radial Basis Function (RBF) neural network for chaotic time series is presented in this paper. The predictive model of chaotic time series is established with the adaptive RBF neural networks and the steps of the chaotic learning algorithm with adaptive RBF neural networks are expressed. The network system can enhance the stabilization and associative memory of chaotic dynamics and generalization ability of predictive model even by imperfect and variation inputs during the learning and prediction process by selecting the suitable nonlinear feedback term. The dynamics of network become chaotic one in the weight space. Simulation experiments of chaotic time series produced by Lorenz equation are proceeded by a RBF neural network.The experimental and simulating results indicated that the forecast method of the adaptive RBF neutral network compared with the forecast method of back propagation (BP) neutral network based on the chaotic learning algorithm has faster learning capacity and higher accuracy of forecast．The method provides a new way for the chaotic time series prediction.

Index Terms—Chaos Theory, Phase Space Reconstruction, Time Series Prediction, RBF Neural network, Algorithm

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Cite: Lisheng Yin, Yigang He, Xueping Dong, and Zhaoquan Lu, " Adaptive Chaotic Prediction Algorithm of RBF Neural Network Filtering Model based on Phase Space Reconstruction," Journal of Computers vol. 8, no. 6, pp. 1449-1455, 2013.