Global asymptotic stability analysis for cellular neural networks with time delays

This paper provides a new sufficient condition for the global asymptotic stability and uniqueness of the equilibrium point of cellular neural networks with time delays. This condition is 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.