Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
411789 | Neurocomputing | 2015 | 6 Pages |
Abstract
This paper focuses on the problem of delay-dependent dissipativity analysis for a class of neural networks with time-varying delays. A free-matrix-based inequality method is developed by introducing a set of slack variables, which can be optimized via existing convex optimization algorithms. Then, by employing Lyapunov functional approach, sufficient conditions are derived to guarantee that the considered neural networks are strictly (Q,S,R)(Q,S,R)-γ-dissipative. The conditions are presented in terms of linear matrix inequalities and can be readily checked and solved. Numerical examples are finally provided to demonstrate the effectiveness and advantages of the proposed new design techniques.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hong-Bing Zeng, Yong He, Peng Shi, Min Wu, Shen-Ping Xiao,