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International Conference on Intelligent Computing

ICIC 2006: Intelligent Computing pp 216–226Cite as

Novel Delay-Dependent Exponential Stability Analysis for a Class of Delayed Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4113))

Abstract

This paper deals with the problem of exponential stability for a class of delayed neural networks described by nonlinear delay differential equations of neutral type. A less conservative exponential stability condition is derived based on a new Lyapunov-Krasovskii functional in term of linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed methods.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering & Automation, Tianjin University, Tianjin, 300072, China

    Zhiqiang Zuo & Yijing Wang

Authors
  1. Zhiqiang Zuo
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  2. Yijing Wang
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China

    De-Shuang Huang

  2. Carnegie Mellon University,  

    Kang Li

  3. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Stranmillis Road, BT9 5AH, Belfast, UK

    George William Irwin

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© 2006 Springer-Verlag Berlin Heidelberg

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Zuo, Z., Wang, Y. (2006). Novel Delay-Dependent Exponential Stability Analysis for a Class of Delayed Neural Networks. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_21

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  • DOI: https://doi.org/10.1007/11816157_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37271-4

  • Online ISBN: 978-3-540-37273-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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