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Global exponential stability of a class of Hopfield neural networks with delays

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Abstract

This paper investigates global exponential stability of a class of Hopfield neural networks with delays based on contraction mapping principle, Lyapunov function and inequality technique. Some sufficient conditions are derived that ensure the existence, uniqueness, global exponential stability of equilibrium point of the neural networks. Finally, an illustrative numerical example is given to demonstrate the effectiveness of our results.

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Acknowledgments

The authors would like to thank the anonymous reviewers and the editor for their constructive comments that led to truly significant improvement of the manuscript. The work is supported by Key Science Foundations of Educational Department of Hubei Province under Grant D20082201 and Z20062202, Youth Project of Educational Department of Hubei Province under Grant Q20102508, Key Project of Chinese Ministry of Education (NO:209078), Innovation Teams of Hubei Normal University.

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Authors and Affiliations

  1. College of Mathematics and Statistics, Hubei Normal University, Huangshi, China

    Wei Liu, Chaojin Fu & Hongchang Hu

Authors
  1. Wei Liu
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  2. Chaojin Fu
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  3. Hongchang Hu
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Correspondence to Chaojin Fu.

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Liu, W., Fu, C. & Hu, H. Global exponential stability of a class of Hopfield neural networks with delays. Neural Comput & Applic 20, 1205–1209 (2011). https://doi.org/10.1007/s00521-010-0470-9

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  • DOI: https://doi.org/10.1007/s00521-010-0470-9

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