Easy to search. Easy to read. Easy to cite with credible sources.
Year: 2009 | Volume: 10 | Issue: 2 | Page No.: 702 - 714
Shengle Fang, Minghui Jiang and Xiaohong Wang
Abstract
In this paper, the problems of determining the global exponential stability and estimating the exponential convergence rate are investigated for a class of neural networks with mixed discrete and distributed time-varying delays. By employing a new Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is exploited to establish sufficient easy-to-test conditions for the neural networks to be globally exponentially stable, which can be readily solved by using the numerically efficient Matlab LMI toolbox. Three numerical examples are provided to demonstrate the effectiveness of the proposed results.