title: Stability analysis of switched cellular neural networks: A mode-dependent average dwell time approach.
abstract: This paper addresses the exponential stability of switched cellular neural networks by using the mode-dependent average dwell time (MDADT) approach. This method is quite different from the traditional average dwell time (ADT) method in permitting each subsystem to have its own average dwell time. Detailed investigations have been carried out for two cases. One is that all subsystems are stable and the other is that stable subsystems coexist with unstable subsystems. By employing Lyapunov functionals, linear matrix inequalities (LMIs), Jessen-type inequality, Wirtinger-based inequality, reciprocally convex approach, we derived some novel and less conservative conditions on exponential stability of the networks. Comparing to ADT, the proposed MDADT show that the minimal dwell time of each subsystem is smaller and the switched system stabilizes faster. The obtained results extend and improve some existing ones. Moreover, the validness and effectiveness of these results are demonstrated through numerical simulations.
authors: College of Mathematics and Computing Science, Changsha University of Science and Technology, Changsha, Hunan 410114, China.
authors: Chuangxia
authors: C
authors: Huang
authors: College of Mathematics and Computing Science, Changsha University of Science and Technology, Changsha, Hunan 410114, China.
authors: Jie
authors: J
authors: Cao
authors: Department of Mathematics, Southeast University, Nanjing 210096, China; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: jdcao@seu.edu.cn.
authors: Jinde
authors: J
authors: Cao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation / Q000639:trends; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology
keywords: Cellular neural network; Lyapunov method; Mode-dependent average dwell time; Stability; Switched system