title: H∞state estimation for memristive neural networks with time-varying delays: The discrete-time case.
abstract: This paper investigates the H∞state estimation problem for a class of discrete-time memristive neural networks (DMNNs) with time-varying delays. For the sake of coping with the switched weight matrices, the DMNNs are recast into a tractable model by defining a series of state-dependent switched signals. Based on the tractable model, the robust analysis method and Lyapunov stability theory are developed to devise a sufficient condition which ensures the global asymptotical stability of the estimation error system with a prescribed H∞performance. The desired state estimator gain matrix and optimal performance index can be accomplished via solving a convex optimization problem subject to several linear matrix inequalities (LMIs). Finally, one numerical example is presented to check the effectiveness of the theoretical results.
authors.affiliation: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China. Electronic address: dingsanbo@163.com.
authors.forename: Sanbo
authors.initials: S
authors.lastname: Ding
authors.affiliation: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China. Electronic address: zhanshan_wang@163.com.
authors.forename: Zhanshan
authors.initials: Z
authors.lastname: Wang
authors.affiliation: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China. Electronic address: kfwjd@ncwu.edu.cn.
authors.forename: Jidong
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China. Electronic address: hgzhang@ieee.org.
authors.forename: Huaguang
authors.initials: H
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013997:Time Factors
keywords:  state estimation; Discrete-time memristive neural networks; Robust analysis method; Time-varying delays