Input‐to‐state ℋ∞ learning of recurrent neural networks with delay and disturbance. (16th April 2021)

• Record Type: Journal Article
• Authors: Zhang, Zhi; Huang, Xin; Chen, Yebin; Zhou, Jianping
• Is Part Of: International journal of adaptive control and signal processing. Volume 35:Number 8(2021)
• Page Start: 1438
• Page End: 1453
• Publication Date: 2021-04-16
• Subjects: ℒ2‐gain -- delay -- input‐to‐state stability -- recurrent neural networks -- weight learning; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3251
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: This article deals with the issue of input‐to‐state ℋ ∞ stabilization for recurrent neural networks with delay and external disturbance. The goal is to design a suitable weight‐learning law to make the considered network input‐to‐state stable with a predefined ℒ 2 ‐gain. Based on the solution of linear matrix inequalities, two schemes for the desired learning law are presented via using decay‐rate‐dependent and decay‐rate‐independent Lyapunov functionals, respectively. It is shown that, in the absence of external disturbance, the proposed learning law also guarantees the exponential stability of the network. To illustrate the applicability of the present weight‐learning law, two numerical examples with simulations are given.