New global exponential stability criteria for interval-delayed neural networks

This paper is concerned with the problem of global robust exponential stability analysis for a class of interval cellular neural networks with time delay. By introducing a novel Lyapunov-Krasovslii function combined 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 globally exponentially stable. Moreover, the conservatism can be notably reduced as the fractioning becomes thinner. Some numerical examples are provided to demonstrate the advantages of the proposed results.