Solving extended linear programming problems using a class of recurrent neural networks

Xiaolin Hu, Jun Wang

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

2 Citations (Scopus)

Abstract

Extended linear programming (ELP) is an extension of classic linear programming in which the decision vector varies within a set. In previous studies in the neural network community, such a set is often assumed to be a box set. In the paper, the ELP problem with a general polyhedral set is investigated, and three recurrent neural networks are proposed for solving the problem with different types of constraints classified by the presence of bound constraints and equality constraints. The neural networks are proved stable in the Lyapunov sense and globally convergent to the solution sets of corresponding ELP problems. Numerical simulations are provided to demonstrate the results.

Original languageEnglish
Title of host publicationNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PublisherSpringer Verlag
Pages994-1003
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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