title: Adaptive synchronization of neural networks with or without time-varying delay.
abstract: In this paper, based on the invariant principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the synchronization of almost all kinds of coupled identical neural networks with time-varying delay, which can be chaotic, periodic, etc. We do not assume that the concrete values of the connection weight matrix and the delayed connection weight matrix are known. We show that two coupled identical neural networks with or without time-varying delay can achieve synchronization by enhancing the coupling strength dynamically. The update gain of coupling strength can be properly chosen to adjust the speed of achieving synchronization. Also, it is quite robust against the effect of noise and simple to implement in practice. In addition, numerical simulations are given to show the effectiveness of the proposed synchronization method.
authors.affiliation: Department of Mathematics, Southeast University, Nanjing 210096, China. jdcao@seu.edu.cn
authors.forename: Jinde
authors.initials: J
authors.lastname: Cao
authors.forename: Jianquan
authors.initials: J
authors.lastname: Lu
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000222:Adaptation, Physiological / Q000502:physiology; D000818:Animals; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D003344:Cortical Synchronization; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D009435:Synaptic Transmission / Q000502:physiology*; D013997:Time Factors