Novel stability of cellular neural networks with interval time-varying delay
In this paper, the asymptotic stability is investigated for a class of cellular neural networks with interval time-varying delay (that is, 0 < h(1) < d(t) < h(2)). By introducing a novel Lyapunov functional with the idea of partitioning the lower bound h, of the time-varying delay, a new criterion of asymptotic stability is derived in terms of a linear matrix inequality (LMI), which can be efficiently solved via standard numerical software. The criterion proves to be less conservative than most of the existing results, and the conservatism could be notably reduced by thinning the delay partitioning. Two examples are provided to demonstrate the less conservatism and effectiveness of the proposed stability conditions. (C) 2008 Elsevier Ltd. All rights reserved.
cellular neural networks interval time-varying delay linear matrix inequality delay partitioning global asymptotic stability