Impulsive stabilization of high-order hopfield-type neural networks with time-varying delays.
This paper studies the problems of global exponential stability for impulsive high-order Hopfield-type neural networks (NNs) with time-varying delays. By employing the Lyapunov-Razumikhin technique, some criteria ensuring global exponential stability are derived. Our results are then used to obtain some sufficient conditions under which some NNs can be forced to converge by impulsive control. Numerical examples are also discussed to illustrate our results.
Liu
Xinzhi
X
Department of Applied Mathematics, University of Waterloo. Waterloo, ON N2L 3G1, Canada. xzliu@math.uwaterloo.ca
Wang
Qing
Q
D000465:Algorithms; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted