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A new approach to exponential stability analysis of neural networks with time-varying delays

โœ Scribed by Shengyuan Xu; James Lam

Publisher
Elsevier Science
Year
2006
Tongue
English
Weight
143 KB
Volume
19
Category
Article
ISSN
0893-6080

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โœฆ Synopsis

This paper considers the problem of exponential stability analysis of neural networks with time-varying delays. The activation functions are assumed to be globally Lipschitz continuous. A linear matrix inequality (LMI) approach is developed to derive sufficient conditions ensuring the delayed neural network to have a unique equilibrium point, which is globally exponentially stable. The proposed LMI conditions can be checked easily by recently developed algorithms solving LMIs. Examples are provided to demonstrate the reduced conservativeness of the proposed results.

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Global exponential stability for impulsive cellular neural networks with time-varying delays
โœ Shair Ahmad; Ivanka M. Stamova ๐Ÿ“‚ Article ๐Ÿ“… 2008 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 245 KB

In this paper, we study the problem of global exponential stability for cellular neural networks (CNN) with time-varying delays and fixed moments of impulsive effect. We establish several stability criteria by employing Lyapunov functions and the Razumikhin technique.