On dissipative filtering over unreliable communication links for stochastic jumping neural networks based on a unified design method. Issue 17 (November 2016)

• Record Type: Journal Article
• Authors: Chen, Mengshen; Shen, Hao; Li, Feng
• Is Part Of: Journal of the Franklin Institute. Volume 353:Issue 17(2016:Nov.)
• Page Start: 4583
• Page End: 4601
• Publication Date: 2016-11
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2016.08.020
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper is concerned with the dissipative filtering problem for a class of stochastic jumping neural networks. The model under consideration is subject to unreliable communication links, which result in some network-induced phenomena such as packet dropouts, sensor nonlinearity and unknown / partly known mode information. A set of Bernoulli distributed white sequences are introduced to govern these phenomena occurring in a random way. The aim is to design a mixed filter, which ensures that the filtering error system is extended stochastically dissipative. Such a mixed filter has the advantages of both the model independent filter and the asynchronous filter. With the help of Lyapunov–Krasovskii methodology and an improved matrix decoupling approach, sufficient conditions for the existence of such a filter are presented by solving some convex optimization problems. A numerical example is given to verify the effectiveness of the proposed method.