Stability and synchronization of memristor-based fractional-order delayed neural networks.
Global asymptotic stability and synchronization of a class of fractional-order memristor-based delayed neural networks are investigated. For such problems in integer-order systems, Lyapunov-Krasovskii functional is usually constructed, whereas similar method has not been well developed for fractional-order nonlinear delayed systems. By employing a comparison theorem for a class of fractional-order linear systems with time delay, sufficient condition for global asymptotic stability of fractional memristor-based delayed neural networks is derived. Then, based on linear error feedback control, the synchronization criterion for such neural networks is also presented. Numerical simulations are given to demonstrate the effectiveness of the theoretical results.
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
Liping
L
Chen
School of Mathematics, Anhui University, Hefei 230039, China.
Ranchao
R
Wu
Department of Mathematics, Southeast University, Nanjing 210096, China; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: jdcao@seu.edu.cn.
Jinde
J
Cao
School of Mathematics, Anhui University, Hefei 230039, China.
Jia-Bao
JB
Liu
D000465:Algorithms; D004867:Equipment Design; D005246:Feedback; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics
Fractional-order; Memristor-based neural networks; Stability; Synchronization