Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408439 | Neurocomputing | 2011 | 5 Pages |
Abstract
This paper deals with the problem of passivity analysis for neural networks with both time-varying delay and norm-bounded parameter uncertainties by employing an improved free-weighting matrix approach. Some useful terms have been retained, which were used to be ignored in the derivative of Lyapunov–Krasovskii functional. Furthermore, the relationship among the time-varying delay, its upper bound and their difference is taken into account. As a result, for two types of time-varying delays, less conservative delay-dependent passivity conditions are obtained in terms of linear matrix inequalities (LMIs), respectively. Finally, a numerical example is given to demonstrate the effectiveness of the proposed techniques.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hong-Bing Zeng, Yong He, Min Wu, Shen-Ping Xiao,