\(H_{\infty }\) filtering for discrete-time fuzzy stochastic neural networks with mixed time-delays
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- 作者：Yajun Li ; Wenping Xiao ; Jingzhao Li…
- 关键词：Asymptotically mean square stable ; Stochastic discrete neural networks ; Linear matrix inequality(LMI) ; \(H_{\infty }\) ; filtering ; Fuzzy systems
- 刊名：Journal of Applied Mathematics and Computing
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：52
- 期：1-2
- 页码：1-26
- 全文大小：614 KB
- 刊物类别：Mathematics and Statistics
- 刊物主题：Mathematics
Computational Mathematics and Numerical Analysis
Applied Mathematics and Computational Methods of Engineering
Theory of Computation
Mathematics of Computing - 出版者：Springer Berlin / Heidelberg
- ISSN：1865-2085
- 卷排序：52
文摘
The \(H_{\infty }\) filter problem for a class of fuzzy stochastic discrete neural networks system with mixed delays is studied in this paper. The mixed delays consist of discrete and distributed delays. Based on discrete inequality technic and the Lyapunov–Krasovskii functional approach, sufficient conditions for the existence of admissible filters are established in terms of linear matrix inequalities, which ensure the asymptotical mean-square stability as well as a prescribed \(H_{\infty }\) disturbance attenuation level. Examples and simulations are provided to illustrate the effectiveness of the proposed method.