Exponential stability and extended dissipativity criteria for generalized discrete-time neural networks with additive time-varying delays

Yaonan Shan, Kun She, Shouming Zhong, Qishui Zhong, Kaibo Shi and Can Zhao

Applied Mathematics and Computation, 2018, vol. 333, issue C, 145-168

Abstract: This paper is concerned with exponential stability and extended dissipativity criteria for generalized discrete-time neural networks (GDNNs) with additive time-varying delays. The generalized dissipativity analysis combines a few previous results into a framework, such as l2−l∞ performance, H∞ performance, passivity performance, strictly (Q,S,R)−γ−dissipative and strictly (Q,S,R)−dissipative. The definition of exponential stability for GDNNs is given with a new and more appropriate expression. A novel augmented Lyapunov-Krasovskii functional (LKF) which involves more information about the additive time-varying delays is constructed. By introducing more zero equalities and using a new double summation inequality together with Finsler’s lemma, an improved delay-dependent exponential stability and extended dissipativity criterion are derived in terms of convex combination technique (CCT). Finally, numerical examples are given to illustrate the usefulness and advantages of the proposed methods.

Keywords: Generalized discrete-time neural networks (GDNNs); Additive time-varying delays; Exponential stability; Extended dissipativity; Summation inequality (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:333:y:2018:i:c:p:145-168

DOI: 10.1016/j.amc.2018.03.101

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