title: Extended dissipative state estimation for uncertain discrete-time Markov jump neural networks with mixed time delays.
abstract: This paper is concerned with the problem of extended dissipativity-based state estimation for uncertain discrete-time Markov jump neural networks with finite piecewise homogeneous Markov chain and mixed time delays. The aim of this paper is to present a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative. A triple-summable term is introduced in the constructed Lyapunov function and the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term. The extended dissipativity criterion is derived in form of linear matrix inequalities. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method.
authors.affiliation: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China. Electronic address: liqian0210@126.com.
authors.forename: Qian
authors.initials: Q
authors.lastname: Li
authors.affiliation: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China. Electronic address: qxzhu@uestc.edu.cn.
authors.forename: Qingxin
authors.initials: Q
authors.lastname: Zhu
authors.affiliation: School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.
authors.forename: Shouming
authors.initials: S
authors.lastname: Zhong
authors.affiliation: School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.
authors.forename: Fuli
authors.initials: F
authors.lastname: Zhong
keywords: Extended dissipative state estimation; Markov jump neural networks; Piecewise homogeneous.