Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6421542 | Applied Mathematics and Computation | 2013 | 10 Pages |
Abstract
This paper deals with the problem of the robust dissipativity analysis for delayed neural networks with randomly occurring uncertainties. The randomly occurring uncertainties under consideration are assumed to follow certain mutually uncorrelated Bernoulli distributed white noise sequences. By using reciprocally convex approach combined with an extended Wirtinger inequality, some delay-dependent conditions for the concerned neural networks to be stochastically strictly (Q, S, R)-θ-dissipative are established. Finally, two numerical examples are given to illustrate the reduced conservatism and effectiveness of our proposed approach.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Jing Wang, Ju H. Park, Hao Shen, Jian Wang,