Global Robust Exponential Stability of Cohen-Grossberg Neural Network with Time Varying Delays

Abstract:

In this paper, the global robust exponential stability is discussed for Cohen-Grossgerg neural network with parameter uncertainties and 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 the main condition to ensure the global robust exponential stability of the equilibrium point for this system. The proposed result is less restrictive, easier to check in practice. Remarks are made with other previous works to show the superiority of the obtained result, and the simulation example is used to demonstrate the effectiveness of our result.

• Periodical: Applied Mechanics and Materials (Volumes 182-183)
• Pages: 1135-1140
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.182-183.1135
• Online since: June 2012
• Authors: Rui Zhang, Chang Tao Wang
• Keywords: Cohen-Grossberg Neural Network, Global Robust Exponential Stability, Linear Matrix Inequality (LMI), Lyapunov Functional

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