Further results on fixed/preassigned-time projective lag synchronization control of hybrid inertial neural networks with time delays |
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| Institution: | 1. School of Mathematics and Statistics, South-Central Minzu University, Wuhan 430074, China;2. School of Mathematics, Southeast University, Nanjing 210096, China;3. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;4. Department of Mathematics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan;1. PPGEE, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil;2. Université de Lorraine, CNRS, CRAN, Nancy F-54000, France;1. School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China;2. Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan 430200, China;3. Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;4. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China |
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| Abstract: | This article aims to study fixed-time projective lag synchronization(FXPLS) and preassigned-time projective lag synchronization(PTPLS) of hybrid inertial neural networks(HINNs) with state-switched and discontinuous activation functions(DAFs). By constructing new hybrid fixed-time control and based on theory of non-smooth analysis, we achieve novel results on FXPLS for such HINNs. Through designing novel hybrid preassigned-time control, new criteria on PTPLS of the HINNs is also taken into account. And as distinct from recent works, the FXPLS and PTPLS results are established via non-variable substitution and in a more generalized framework than common synchronization, which also has more extensive practical applications. Finally, example simulations are displayed to set forth the validity of the acquired FXPLS and PTPLS. |
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