Exponential stability of impulsive high-order Hopfield-type neural networks with time-varying delays.
This paper considers the problems of global exponential stability and exponential convergence rate for impulsive high-order Hopfield-type neural networks with time-varying delays. By using the method of Lyapunov functions, some sufficient conditions for ensuring global exponential stability of these networks are derived, and the estimated exponential convergence rate is also obtained. As an illustration, an numerical example is worked out using the results obtained.
Liu
Xinzhi
X
Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.
Teo
Kok Lay
KL
Xu
Bingji
B
D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors