Global exponential stability of Cohen-Grossberg neural network with time varying delays

Authors:
Rui Zhang;Yuanwei Jing;Zhanshan Wang
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang;School of Information Science and Engineering, Northeastern University, Shenyang;School of Information Science and Engineering, Northeastern University, Shenyang
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

In this paper, the global exponential stability is discussed for Cohen-Grossgerg neural network with time varying delays. On the basis of the linear matrix inequalities (LMIs) technique, and Lyapunov functional method combined with the Bellman inequality and Jensen inequality technique, we have obtained two main conditions to ensure the global exponential stability of the equilibrium point for this system, one of which is dependent on the change rate of time varying delays, and the other is dependent on the upper bound of time varying delays. The proposed results are less restrictive than those given in the earlier literatures, easier to check in practice, and suitable of the cases of slow or fast time varying delays. Remarks are made with other previous works to show the superiority of the obtained results, and the simulation examples are used to demonstrate the effectiveness of our results.