Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8900858 | Applied Mathematics and Computation | 2018 | 24 Pages |
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 l2âlâ performance, Hâ performance, passivity performance, strictly (Q,S,R)âγâdissipative and strictly (Q,S,R)âdissipative. 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
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Yaonan Shan, Kun She, Shouming Zhong, Qishui Zhong, Kaibo Shi, Can Zhao,