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

Abstract:

In this paper, the global robust exponential stability is discussed for interval 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 the main condition to ensure the global robust exponential stability of the equilibrium point for this system. The obtained stability criterion is dependent on the upper bound of time varying delays. The proposed result is less restrictive, and suitable of the cases of slow or fast time varying delays, 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: Advanced Materials Research (Volumes 756-759)
• Pages: 3884-3888
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.3884
• Online since: September 2013
• Authors: Rui Zhang, Jian Liu, Meng Xin Li, Mei Ju Liu
• Keywords: Global Robust Exponential Stability, Interval Cohen-Grossberg Neural Network, Linear Matrix Inequality (LMI), Lyapunov Functional

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