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A novel criterion for global asymptotic stability of cellular neural networks with time delays

This paper provided a new sufficient condition for the global asymptotic stability of cellular neural networks (CNNs) with time delays. An asymptotic stability of CNNs with time delays was considered by constructing a new suitable Lyapunov functional and some matrix inequality technique. A novel delay-independent stability criterion which considered the excitatory and inhibitory connection was given in terms of matrix inequalities. This condition was expressed in terms of linear matrix inequalities, which can be easily checked by various recently developed algorithms in solving convex optimization problems. Numerical examples are provided to show that the proposed stability result is less conservative than some previously established ones in the literature.