Title :
Exponential stability of impulsive high-order Hopfield-type neural networks with time-varying delays
Author :
Liu, Xinzhi ; Teo, Kok Lay ; Xu, Bingji
Author_Institution :
Dept. of Appl. Math., Univ. of Waterloo, Ont., Canada
Abstract :
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.
Keywords :
Hopfield neural nets; Lyapunov methods; asymptotic stability; convergence; delay estimation; neural nets; numerical stability; time-varying systems; Lyapunov function; exponential convergence rate; exponential stability; impulsive high-order Hopfield-type neural network; time-varying delay; Convergence; Delay effects; Hopfield neural networks; Lyapunov method; Mathematics; Neural networks; Neurons; Pattern recognition; Stability; Sufficient conditions; Exponential stability; impulsive high-order Hopfield-type Lyapunov function; neural networks; Algorithms; Computer Simulation; Models, Statistical; Neural Networks (Computer); Nonlinear Dynamics; Signal Processing, Computer-Assisted; Time Factors;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.857949
