Point-sampled-data passivity stabilization of stochastic complex-valued memristor networks with multi-delays and reaction-diffusion term: A switching model approach |
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| Institution: | 1. School of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing, Jiangsu, 210023, People’s Republic of China;2. Faculty of Information and Technology, Beijing University of Technology, Beijing, 100124, China;3. School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, 610106, China;4. School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China;5. Key Laboratory of HPC-SIP (MOE), School of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, Hunan, China;1. School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China;2. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China;1. School of Artificial Intelligence, Shenyang University of Technology, Shenyang, Liaoning, 110870, China;2. School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, China;1. School of Mathematics and Statistics & FJKLMAA, Fujian Normal University, Fuzhou 350117, PR China;2. School of Mathematical Sciences, South China Normal University, Guangzhou 510631, PR China;1. School of Electrical Engineering, Guangxi University, Nanning 530004, China;2. Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, China;3. School of Mathematics and Information Science, Guangxi University, Nanning 530004, China;4. Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, China |
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| Abstract: | This paper focuses on the stochastic passivity problem of stochastic memristor-based complex valued neural networks with two different types of time-delays and reaction-diffusion terms by sampled-data control strategy. Different from the existing sampled-data strategies, this paper develops spatial and temporal point sampling, namely, only a finite number of points in space or time are sampled. By introducing two different Lyapunov functional and employing techniques such as Wirtinger’s integral inequality, Jensen’s inequality and Young’s inequality, etc., two different sufficient conditions for the stochastic passivity of the system are established. Prominently, the condition quantitatively reveals the relationship between the upper and lower bounds of the sampling interval at spatial and temporal points. Finally, a numerical example is given to verify the rationality of the proposed method. Notice, compared with a large number of results of real-valued reaction-diffusion neural networks, the research results of sampled-data controlled complex-valued reaction-diffusion neural networks have not appeared so far, and this work is the first attempt to fill in the gaps in this topic. |
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