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Exponential stability analysis of Cohen-Grossberg neural networks with time-varying delays

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Abstract

In this paper, we study Cohen-Grossberg neural networks (CGNN) with time-varying delay. Based on Halanay inequality and continuation theorem of the coincidence degree, we obtain some sufficient conditions ensuring the existence, uniqueness, and global exponential stability of periodic solution. Our results complement previously known results.

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Authors and Affiliations

  1. College of Mathematics and Econometrics, Hunan University, Changsha, Hunan, 410082, China

    Yi-min Meng, Li-hong Huang & Zhao-hui Yuan

Authors
  1. Yi-min Meng
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  2. Li-hong Huang
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  3. Zhao-hui Yuan
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Corresponding author

Correspondence to Yi-min Meng.

Additional information

Supported by the National Natural Science Foundation of China (No. 11071060) and Key Program of Application Science Foundation of Hunan Province (No. 2008FJ2008).

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Meng, Ym., Huang, Lh. & Yuan, Zh. Exponential stability analysis of Cohen-Grossberg neural networks with time-varying delays. Acta Math. Appl. Sin. Engl. Ser. 28, 181–192 (2012). https://doi.org/10.1007/s10255-012-0133-y

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  • DOI: https://doi.org/10.1007/s10255-012-0133-y

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