title: State estimation for static neural networks with time-varying delay.
abstract: 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.
authors.affiliation: School of Electronics and Information Engineering, Soochow University, Suzhou, PR China. cshhuang@gmail.com
authors.forename: He
authors.initials: H
authors.lastname: Huang
authors.forename: Gang
authors.initials: G
authors.lastname: Feng
authors.forename: Jinde
authors.initials: J
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer; D013997:Time Factors