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Dynamics of Competitive Neural Networks with Inverse Lipschitz Neuron Activations

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Advances in Neural Networks - ISNN 2010 (ISNN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6063))

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Abstract

In this paper, by using nonsmooth analysis approach, topological degree theory and Lyapunov-Krasovskii function method, the issue of global exponential stability is investigated for competitive neural networks possessing inverse Lipschitz neuron activations. Several novel sufficient conditions are established towards the existence, uniqueness and global exponential stability of the equilibrium point for competitive neural networks with time-varying delay.

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Author information

Authors and Affiliations

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

    Xiaobing Nie & Jinde Cao

Authors
  1. Xiaobing Nie
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  2. Jinde Cao
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Bao-Liang Lu

  3. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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© 2010 Springer-Verlag Berlin Heidelberg

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Nie, X., Cao, J. (2010). Dynamics of Competitive Neural Networks with Inverse Lipschitz Neuron Activations. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_62

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  • DOI: https://doi.org/10.1007/978-3-642-13278-0_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13277-3

  • Online ISBN: 978-3-642-13278-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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