Quasi-synchronization of heterogeneous stochastic coupled reaction-diffusion neural networks with mixed time-varying delays via boundary control |
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| Institution: | 1. School of Mathematics and Statistics, Ningxia University, Yinchuan 750021, China;2. Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China;3. School of Mechanical and Electronical Engineering, Lanzhou University of Technology, Lanzhou 730050, China;1. School of Automation, Southeast University, Nanjing 210096, China;3. Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu 212013, China;1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Beijing Key laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China;3. Shunde Graduate School, University of Science and Technology Beijing, Foshan 528399, China;4. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Ploytechnical University, Xi’an 710072, PR China;5. College of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China;6. School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China;7. Institute of Physics, Humboldt-University, Berlin 10099, Germany;8. Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany |
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| Abstract: | In this paper, the quasi-synchronization problem of heterogeneous stochastic coupled neural networks (HSCNNs) is discussed. The effects of the mixed time-varying delay and diffusion phenomenon on the system are considered separately in time and space. Moreover, different from the previous distributed control, boundary control is introduced to realize network synchronization. This not only reduces the space cost of the controller, but also makes it easier to implement. Thus, the mean-square quasi-synchronization of HSCNNs is guaranteed by using matrix inequality and stochastic analysis tools. In addition to focusing on systems with Neumann boundary conditions, we briefly investigate HSCNNs with time-invariant delays and mixed boundary conditions respectively, and provide sufficient conditions to achieve the desired performance. Finally, the correctness of the conclusion is verified by several examples. |
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