State estimation for static neural networks with time-varying delay.
This paper is concerned with the state estimation problem for a class of static neural networks with time-varying delay. Here the time derivative of the time-varying delay is no longer required to be smaller than one. A delay partition approach is proposed to derive a delay-dependent condition under which the resulting error system is globally asymptotically stable. The design of a desired state estimator for such kinds of delayed neural networks can be accomplished by means of solving a linear matrix inequality. A simulation example is finally given to show the application of the developed approach.
Huang
He
H
School of Electronics and Information Engineering, Soochow University, Suzhou, PR China. cshhuang@gmail.com
Feng
Gang
G
Cao
Jinde
J
D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer; D013997:Time Factors