title: Global exponential stability for switched memristive neural networks with time-varying delays.
abstract: This paper considers the problem of exponential stability for switched memristive neural networks (MNNs) with time-varying delays. Different from most of the existing papers, we model a memristor as a continuous system, and view switched MNNs as switched neural networks with uncertain time-varying parameters. Based on average dwell time technique, mode-dependent average dwell time technique and multiple Lyapunov-Krasovskii functional approach, two conditions are derived to design the switching signal and guarantee the exponential stability of the considered neural networks, which are delay-dependent and formulated by linear matrix inequalities (LMIs). Finally, the effectiveness of the theoretical results is demonstrated by two numerical examples.
authors.affiliation: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China.
authors.forename: Youming
authors.initials: Y
authors.lastname: Xin
authors.affiliation: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China. Electronic address: yuxiali2004@aliyun.com.
authors.forename: Yuxia
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China.
authors.forename: Zunshui
authors.initials: Z
authors.lastname: Cheng
authors.affiliation: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China.
authors.forename: Xia
authors.initials: X
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013997:Time Factors; D035501:Uncertainty
keywords: Average dwell time; Exponential stability; Linear matrix inequalities; Memristive neural networks; Switched system