2002•Unpublished venueRequires access

Exponential stability for delayed cellular neural networks

Xiaoping Li, Licheng Jiao

Abstract

A new sufficient condition for global exponential stability and lower bounds on the rate of exponential convergence of delayed cellular neural networks (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functionals, is simple and effective for the stability analysis of neural networks with delay. Some previously established results in the literature are shown to be special cases of the presented result.

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Key concepts: Exponential stability, Cellular neural network, Artificial neural network, Convergence (economics), Simple (philosophy), Control theory (sociology), Stability (learning theory), Applied mathematics