Fast fixed-time synchronization control analysis for a class of coupled delayed Cohen-Grossberg neural networks |
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| Institution: | 1. School of Computer Science and Technology, Huaiyin Normal University, Huaian 223300, Jiangsu, China;2. School of Mathematics, Southeast University, Nanjing 210096, China;3. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;4. School of Mathematical Science, Huaiyin Normal University, Huaian 223300, Jiangsu, China;1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China;2. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;1. College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China;2. Centre for Artificial Intelligence, University of Technology Sydney, Ultimate 2007, Australia |
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| Abstract: | In a fixed-time control system, the convergence rate and the fixed settling time are two important performance indexes. In this paper, a novel fixed-time control law is proposed and designed to control a class of coupled delayed Cohen-Grossberg neural networks to achieve synchronization with fast convergence rate within a fixed settling time. It should be emphasized that the derived settling time approach can provide a tighter settling time to more effectively reflect the performance for fast convergence rate of the considered controlled system. Moreover, to show the advantages of the proposed fixed-time control law and the derived fixed settling time approach, the existing related control laws and fixed settling time approaches are further discussed. In addition, the obtained fixed-time synchronization control theory is applied to a secure communication scenario, which further shows the feasibility and innovation of the addressed theoretical results. |
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