Global Ezponential Stability of Cohen-Grossberg Neural Network with Time Varying Delays
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.
Cohen-Grossberg Neural Network Lyapunov Functional Global Ezponential Stability Bellman Inequality Jenson Inequality Linear Matriz Inequalities (LMIs)
Rui Zhang Yuanwei Jing Zhanshan Wang
School of Information Science and Engineering, Northeastern University, Shenyang 110004
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3177-3182
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）