Extended dissipative state estimation for uncertain discrete-time Markov jump neural networks with mixed time delays

Qian Li; Qingxin Zhu; Shouming Zhong; Fuli Zhong

doi:10.1016/j.isatra.2016.11.004

Extended dissipative state estimation for uncertain discrete-time Markov jump neural networks with mixed time delays

ISA Trans. 2017 Jan:66:200-208. doi: 10.1016/j.isatra.2016.11.004.

Authors

Qian Li¹, Qingxin Zhu², Shouming Zhong³, Fuli Zhong⁴

Affiliations

¹ School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China. Electronic address: liqian0210@126.com.
² 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.
³ School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.
⁴ School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.

PMID: 27916268
DOI: 10.1016/j.isatra.2016.11.004

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.

Keywords: Extended dissipative state estimation; Markov jump neural networks; Piecewise homogeneous..