Dynamical Pattern Classification of Lorenz System and Chen System

Authors:
Hao Cheng;Cong Wang
Affiliations:
College of Automation and the Center for Control and Optimization, South China University of Technology, Guangzhou, P.R. China 510641;College of Automation and the Center for Control and Optimization, South China University of Technology, Guangzhou, P.R. China 510641
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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Abstract

Recently, an approach for rapid dynamical pattern recognition was proposed, by which a dynamical pattern can be locally accurately identified and rapidly recognized using localized radial basis function (RBF) networks. Further, a scheme for classification of dynamical patterns was presented. In this paper, we investigate the construction of the recognition system for classification of Lorenz system and Chen system, both of which can generate various types of dynamical patterns. Simulation studies are included to demonstrate the effectiveness of this method.