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Updated: Apr 19, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
Published on: May 25, 2019
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.
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