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Nonlinear Analysis: Real World Applications
Year: 2009  |  Volume: 10  |  Issue: 2  |  Page No.: 702 - 714

Exponential convergence estimates for neural networks with discrete and distributed delays

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.

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