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Global Asymptotic Stability Analysis of Neural Networks with Time-Varying Delays

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Abstract

Global asymptotic stability of the equilibrium point of neural networks with time-varying delays is considered in this paper. By utilizing the Lyapunov--Razumikhin technique, some new sufficient conditions are given. The new criteria do not require the delay function to be differentiable and the activation functions to be bounded or monotone nondecreasing. The results presented here are less restrictive and conservative than those given in the earlier references. Two examples are discussed to compare the present results with the existing ones.

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Authors and Affiliations

  1. Center for Advanced Design Technology, University Key Lab. of Information Science & Engineering, Dalian University, Dalian, 116622, China

    Zhang Qiang, Xiaopeng Wei & Jin Xu

  2. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China

    Zhang Qiang

Authors
  1. Zhang Qiang
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  2. Xiaopeng Wei
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  3. Jin Xu
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Correspondence to Zhang Qiang.

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Qiang, Z., Wei, X. & Xu, J. Global Asymptotic Stability Analysis of Neural Networks with Time-Varying Delays. Neural Process Lett 21, 61–71 (2005). https://doi.org/10.1007/s11063-004-3426-1

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