Reliable filter design for discrete-time neural networks with Markovian jumping parameters and time-varying delay. Issue 5 (March 2020)

• Record Type: Journal Article
• Authors: Xia, Weifeng; Xu, Shengyuan; Lu, Junwei; Zhang, Zhengqiang; Chu, Yuming
• Is Part Of: Journal of the Franklin Institute. Volume 357:Issue 5(2020)
• Page Start: 2892
• Page End: 2915
• Publication Date: 2020-03
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2020.02.039
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper considers the problem of reliable filter design for discrete-time neural networks subject to Markovian jumping parameters and time-varying delay. Firstly, based on a matrix inequality, a new sufficient condition, which guarantees the existence of a reliable filter such that the resulting filtering error system is stochastically stable and extended dissipative, is established. Second, a less conservative stability criterion for neural networks is proposed. Then, the condition for the solvability of the filter design problem is given in terms of linear matrix inequalities (LMIs). Finally, three numerical examples are given to illustrate the effectiveness and advantages of the proposed filter design scheme.