Further results on fixed/preassigned-time projective lag synchronization control of hybrid inertial neural networks with time delays

Zhang G. Cao J. Kashkynbayev A.
September 2023 Elsevier Ltd

Journal of the Franklin Institute
2023 #360 Issue 13 9950 - 9973 pp.

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.

Fixed-time projective lag synchronization , Hybrid inertial neural networks , Preassigned-time lag projective synchronization , Time delays

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School of Mathematics and Statistics, South-Central Minzu University, Wuhan, 430074, China
School of Mathematics, Southeast University, Nanjing, 210096, China
Yonsei Frontier Lab, Yonsei University, Seoul, 03722, South Korea
Department of Mathematics, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan

School of Mathematics and Statistics
School of Mathematics
Yonsei Frontier Lab
Department of Mathematics

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