Global stability of a recurrent neural network for solving pseudomonotone variational inequalities

Xiaolin Hu, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Solving variational inequality problems by using neural networks are of great interest in recent years. To date, most work in this direction focus on solving monotone variational inequalities. In this paper, we show that an existing recurrent neural network proposed originally for solving monotone variational inequalities can be used to solve pseudomonotone variational inequalities with proper choice of a system parameter. The global convergence, global asymptotic stability and global exponential stability of the neural network are discussed under various conditions. The existing stability results are thus extended in view of the fact that pseudomonotonicity is a weaker condition than monotonicity.

Original languageEnglish
Title of host publicationISCAS 2006
Subtitle of host publication2006 IEEE International Symposium on Circuits and Systems, Proceedings
Pages755-758
Number of pages4
Publication statusPublished - 2006
Externally publishedYes
EventISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems - Kos, Greece
Duration: 21 May 200624 May 2006

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Conference

ConferenceISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems
Country/TerritoryGreece
CityKos
Period21/05/0624/05/06

Fingerprint

Dive into the research topics of 'Global stability of a recurrent neural network for solving pseudomonotone variational inequalities'. Together they form a unique fingerprint.

Cite this