Two boundary coupling approaches for synchronization of stochastic reaction-diffusion neural networks based on semi-linear PIDEs |
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| Affiliation: | 1. School of Information Science and Technology, and Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong, Linyi University, Linyi 276005, PR China;2. College of Mathematics and System Sciences, Xinjiang University, Urumqi 830000, China;3. School of Mathematics, Southeast University, Nanjing 210096, China;4. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;1. School of Computer Science and Software Engineering, Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin Polytechnic University, Tianjin 300387, China;2. School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin 300387, China;1. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China |
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| Abstract: | This paper studies the exponential synchronization of stochastic reaction-diffusion neural networks based on semi-linear parabolic partial integro-differential equations. Compared with the traditional coupling of states, spatial boundary coupling is designed in this paper. Two kinds of boundary coupling within Neumann boundary conditions are studied, one under the collocated boundary measurement form and the other under the distributed measurement form. Two sufficient conditions for the exponential synchronization using the two kinds of boundary coupling are respectively obtained. Examples are given to show the effectiveness of the proposed spatial boundary coupling. |
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