Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
407181 | Neurocomputing | 2016 | 8 Pages |
Abstract
In this paper, the problem of stability analysis for a class of static recurrent neural networks with interval time-varying delay is considered. By constructing a newly augmented Lyapunov–Krasovskii functional containing triple integral terms and utilizing the inverses of first-order and squared reciprocally convex parameters techniques and zero equality, new and improved delay-dependent stability criteria are proposed to guarantee the asymptotic stability of the concerned networks with the framework of linear matrix inequalities (LMIs). Finally, some numerical examples are given to illustrate the effectiveness of the proposed methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Xin Wang, Kun She, Shouming Zhong, Huilan Yang,