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Globally Exponential Stability of Non-autonomous Delayed Neural Networks

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Pattern Recognition and Image Analysis (IbPRIA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3523))

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Abstract

Globally exponential stability of non-autonomous delayed neural networks is considered in this paper. By utilizing delay differential inequalities, a new sufficient condition ensuring globally exponential stability for non-autonomous delayed neural networks is presented. The condition does not require that the delay function be differentiable or the coefficients be bounded. Due to this reason, the condition improves and extends those given in the previous literature.

The project was supported by the National Natural Science Foundation of China (Grant Nos. 60403001, 60403002)and China Postdoctoral Science Foundation.

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Author information

Authors and Affiliations

  1. University Key Lab of Information Science & Engineering, Dalian University, Dalian, 116622, China

    Qiang Zhang, Xiaopeng Wei & Jin Xu

  2. School of Computer Science and Engineering, Wenzhou Normal College, Wenzhou, 325027, China

    Wenbing Liu

Authors
  1. Qiang Zhang
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  2. Wenbing Liu
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  3. Xiaopeng Wei
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  4. Jin Xu
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Editor information

Editors and Affiliations

  1. Instituto Superior Técnico & Instituto de Sistemas e Robótica,, 1049-001, Lisboa, Portugal

    Jorge S. Marques

  2. ETSI Informática y e Telecomunicación, University of Granada, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  3. Instituto Superior Técnico, CERENA-Centro de Recursos Naturais e Ambiente, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Pedro Pina

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhang, Q., Liu, W., Wei, X., Xu, J. (2005). Globally Exponential Stability of Non-autonomous Delayed Neural Networks. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_12

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  • DOI: https://doi.org/10.1007/11492542_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26154-4

  • Online ISBN: 978-3-540-32238-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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