New global stability criteria for interval delayed neural networks

This paper is concerned with the problem of global robust exponential stability for a class of interval cellular neural networks with time-constant delays. By introducing a novel Lyapunov-Krasovslii functional combining with the idea of delay fractioning, some delay-dependent conditions are derived in terms of the linear matrix inequality, which guarantee the considered interval delayed cellular neural networks to be global exponentially stable. Moreover, the conservatism can be notably reduced as the the fractioning goes thinner. A numerical example is provided to demonstrate the advantage of the proposed result.