Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6865875 | Neurocomputing | 2015 | 9 Pages |
Abstract
In this paper, the stability of complex-valued neural networks with probabilistic time-varying delays is investigated. Two important integral inequalities that include Jensen׳s inequality as a special case are developed. By constructing proper Lyapunov-Krasovskii functional and employing inequality technique, several delay-distribution-dependent sufficient conditions are obtained to guarantee the global asymptotic and exponential stability of the addressed neural networks. These conditions are expressed in terms of complex-valued LMIs, which can be checked numerically using the effective YALMIP toolbox in MATLAB. An example with simulations is given to show the effectiveness of the obtained results.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Qiankun Song, Zhenjiang Zhao, Yurong Liu,