Absolutely exponential stability of a class of neural networks with unbounded delay.
In this paper, the existence and uniqueness of the equilibrium point and absolute stability of a class of neural networks with partially Lipschitz continuous activation functions are investigated. The neural networks contain both variable and unbounded delays. Using the matrix property, a necessary and sufficient condition for the existence and uniqueness of the equilibrium point of the neural networks is obtained. By constructing proper vector Liapunov functions and nonlinear integro-differential inequalities involving both variable delays and unbounded delay, using M-matrix theory, sufficient conditions for absolutely exponential stability are obtained.
Zhang
Jiye
J
National Traction Power Laboratory, Southwest Jiaotong University, Chengdu, China. jyzhang@iis.u-tokyo.ac.jp
Suda
Yoshihiro
Y
Iwasa
Takashi
T
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors