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Dynamical Pattern Classification of Lorenz System and Chen System

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Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5264))

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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.

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Author information

Authors and Affiliations

  1. College of Automation and the Center for Control and Optimization, South China University of Technology, Guangzhou, 510641, P.R. China

    Hao Cheng & Cong Wang

Authors
  1. Hao Cheng
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  2. Cong Wang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghu University, 100084, Beijing, China

    Fuchun Sun

  2. Institute TAMS (Technical Aspects of Multimodal Systems), department of Informatics, University of Hamburg, Vogt-Koelln-Straße 30, 22527, Hamburg, Germany

    Jianwei Zhang

  3. Intel China Research Center, 8/F, Peking University, Department of Machine Intelligence, 100871, Beijing, China

    Ying Tan

  4. Department of Mathematics, Southeast University, 210096, Nanjing, China

    Jinde Cao

  5. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

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© 2008 Springer-Verlag Berlin Heidelberg

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Cheng, H., Wang, C. (2008). Dynamical Pattern Classification of Lorenz System and Chen System. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_37

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  • DOI: https://doi.org/10.1007/978-3-540-87734-9_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87733-2

  • Online ISBN: 978-3-540-87734-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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