A recurrent neural network for non-smooth convex programming subject to linear equality and bound constraints

Qingshan Liu, Jun Wang

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

18 Citations (Scopus)

Abstract

In this paper, a recurrent neural network model is proposed for solving non-smooth convex programming problems, which is a natural extension of the previous neural networks. By using the non-smooth analysis and the theory of differential inclusions, the global convergence of the equilibrium is analyzed and proved. One simulation example shows the convergence of the presented neural network.

Original languageEnglish
Title of host publicationNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PublisherSpringer Verlag
Pages1004-1013
Number of pages10
ISBN (Print)3540464816, 9783540464815
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China
Duration: 3 Oct 20066 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4233 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Neural Information Processing, ICONIP 2006
Country/TerritoryChina
CityHong Kong
Period3/10/066/10/06

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