Exponential stability of cellular neural networks with time-varying delay

Authors:
Meng Hua;Wang Yanhong;Lv Xuan;Xue-Li Wu
Affiliations:
Hebei University of Science and Technology, Engineering Technology Research Centre, Shijiazhuang, Hebei Province;Hebei University of Science and Technology, Engineering Technology Research Centre, Shijiazhuang, Hebei Province;Hebei University of Science and Technology, Engineering Technology Research Centre, Shijiazhuang, Hebei Province;Hebei University of Science and Technology, Engineering Technology Research Centre, Shijiazhuang, Hebei Province and YanShan University, Qinhuangdao
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 2
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An analysis of exponential stability of delayed neural networks with time varying delays

Neural Networks
A generalized LMI-based approach to the global asymptotic stability of delayed cellular neural networks

IEEE Transactions on Neural Networks

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Abstract

A novel method is proposed in this note for exponential stability of cellular neural networks with time-varying delay. New delay-dependent exponential stability conditions of cellular neural network with time-varying delay is presented by constructing Lyapunov function and using linear matrix inequality (LMI). Finally, a numerical example is given to demonstrate the effect of the proposed method.