On dissipative filtering over unreliable communication links for stochastic jumping neural networks based on a unified design method

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 unknownlpartly 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. (C) 2016 The Franklin Institute. Published by Elsevier. Ltd. All rights reserved.