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International Symposium on Neural Networks

ISNN 2008: Advances in Neural Networks - ISNN 2008 pp 40–46Cite as

New Chaos Produced from Synchronization of Chaotic Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5263))

Abstract

In this paper, we investigates synchronization dynamics of neural networks. Generalized linear synchronization (GLS) is proposed to acquire a general kind of proportional relationships between two-neuron networks. Under the point of synchronization, we can find that the node has complex dynamics with some interesting characteristics, and some new chaos phenomenons can been found. Numerical simulations show that this method works very well of two-neuron networks with identical Lorenz systems. Also our method can be applied to other systems.

This work was jointly supported by the Doctoral Found of QUST, and the Natural Science Foundation of Henan Province, China under Grant 0611055100.

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Author information

Authors and Affiliations

  1. School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China

    Zunshui Cheng

Authors
  1. Zunshui Cheng
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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, Z. (2008). New Chaos Produced from Synchronization of Chaotic Neural Networks. 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 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_5

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  • DOI: https://doi.org/10.1007/978-3-540-87732-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87731-8

  • Online ISBN: 978-3-540-87732-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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