Stabilizability of complex complex-valued memristive neural networks using non-fragile sampled-data control |
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| Authors: | Ruimei Zhang Deqiang Zeng Ju H Park Kaibo Shi Yajuan Liu |
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| Institution: | 1. School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China;2. Data Recovery Key Laboratory of Sichuan Province, Neijiang Normal University, Neijiang 641100, PR China;3. School of Mathematics Sciences, Sichuan Normal University, Chengdu 610066, China;4. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea;5. School of Information Science and Engineering, Chengdu University, Chengdu 610106, China;6. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, PR China;1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China;2. School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China;3. School of Science, Huzhou Teachers College, Huzhou 313000, China;1. Department of Mathematics, Harbin Institute of Technology (Weihai), Weihai 264209, China;2. College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China;1. College of Mathematics and Econometrics, Hunan University, Changsha 410082, China;2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;3. Science Program, Texas A & M University at Qatar, Doha 23874, Qatar;1. School of Electrical Engineering and Automation, Tiangong University, Tianjin 300387, China;2. Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tiangong University, Tianjin 300387, China;3. Department of Information Engineering, ShanDong Water Conservancy Vocational College, Rizhao 276826, China;1. School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China;2. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea;3. Data Recovery Key Laboratory of Sichuan Province, and Numerical Simulation Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal University, Neijiang, Sichuan 641100, PR China;4. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, PR China |
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| Abstract: | This paper investigates the stability and stabilizability of complex-valued memristive neural networks (CVMNNs) with random time-varying delays via non-fragile sampled-data control. Taking the influence of gain fluctuations into account, a non-fragile sampled-data controller is designed for CVMNNs. Compared with the existing control schemes, the one here is more applicable and can effectively save the communication resources. The assumption on activation functions of CVMNNs is relaxed by only needing the complex-valued activation functions satisfying the Lipschitz condition. By constructing a suitable Lyapunov–Krasovskii functional (LKF), new stability and stabilizability criteria are derived for CVMNNs. Different from the existing results with the maximum absolute values of memristive connection weights, our ones are based on the average values of the maximum and minimum of the memristive connection weights. Finally, numerical simulations are given to validate the effectiveness of the theoretical results. |
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