Exponential synchronization of a class of neural networks with time-varying delays.
This paper aims to present a synchronization scheme for a class of delayed neural networks, which covers the Hopfield neural networks and cellular neural networks with time-varying delays. A feedback control gain matrix is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory, and its exponential synchronization condition can be verified if a certain Hamiltonian matrix with no eigenvalues on the imaginary axis. This condition can avoid solving an algebraic Riccati equation. Both the cellular neural networks and Hopfield neural networks with time-varying delays are given as examples for illustration.
Cheng
Chao-Jung
CJ
Liao
Teh-Lu
TL
Yan
Jun-Juh
JJ
Hwang
Chi-Chuan
CC
D000818:Animals; D001185:Artificial Intelligence; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009435:Synaptic Transmission / Q000502:physiology*; D013997:Time Factors