title: Receding Horizon Stabilization and Disturbance Attenuation for Neural Networks With Time-Varying Delay.
abstract: This paper is concerned with the problems of receding horizon stabilization and disturbance attenuation for neural networks with time-varying delay. New delay-dependent conditions on the terminal weighting matrices of a new finite horizon cost functional for receding horizon stabilization are established for neural networks with time-varying or time-invariant delays using single- and double-integral Wirtinger-type inequalities. Based on the results, delay-dependent sufficient conditions for the receding horizon disturbance attenuation are given to guarantee the infinite horizon H∞ performance of neural networks with time-varying or time-invariant delays. Three numerical examples are provided to illustrate the effectiveness of the proposed approach.
authors.forename: Choon Ki
authors.initials: CK
authors.lastname: Ahn
authors.forename: Peng
authors.initials: P
authors.lastname: Shi
authors.forename: Ligang
authors.initials: L
authors.lastname: Wu
mesh_terms: D005246:Feedback; D016571:Neural Networks, Computer; D013997:Time Factors