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2009•Journal of Shandong UniversityRequires access

Global asymptotic stability condition for a class of delayed cellular neural networks

Jing Liu

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Abstract

A sufficient condition is obtained for the uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks with constant time delay by using Lyapunov functionals and combining with matrix inequality technique.The condition contains and improves some of the previous results in the literature.

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What this paper is about

A sufficient condition is obtained for the uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks with constant time delay by using Lyapunov functionals and combining with matrix inequality technique.The condition contains and improves some of the previous results in the literature.

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Available abstract

A sufficient condition is obtained for the uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks with constant time delay by using Lyapunov functionals and combining with matrix inequality technique.The condition contains and improves some of the previous results in the literature.

Key concepts: Uniqueness, Exponential stability, Equilibrium point, Mathematics, Class (philosophy), Constant (computer programming), Artificial neural network, Applied mathematics

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