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Global exponential stability for switched memristive neural networks with time-varying delays
Affiliation:1. School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, PR China;2. Data Recovery Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal University, Neijiang 641100, PR China;3. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, PR China;1. School of Science, Jimei University, Xiamen 361021, China;2. Department of Mathematics, Texas A&M University at Qatar, Doha, Qatar;3. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;4. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China;1. School of Sciences, Southwest Petroleum University, Chengdu 610500, China;2. Department of Mathematics, Southeast University, Nanjing 210096, China;3. Department of Electronic Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, China
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.
Keywords:Memristive neural networks  Switched system  Average dwell time  Exponential stability  Linear matrix inequalities
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