A one-layer recurrent neural network for constrained single-ratio linear fractional programming

Qingshan Liu, Jun Wang

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

2 Citations (Scopus)

Abstract

In this paper, a one-layer recurrent neural network is presented for solving single-ration linear fractional programming problems subject to linear equality and box bound constraints. The convergence condition is derived to guarantee the solution optimality to the fractional programming problems if the design parameters in the neural network are larger than the derived lower bounds. Two numerical examples with simulation results show that the proposed neural network is efficient and accurate for solving constrained linear fractional programming problems.

Original languageEnglish
Title of host publication2011 IEEE International Symposium of Circuits and Systems, ISCAS 2011
Pages1089-1092
Number of pages4
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE International Symposium of Circuits and Systems, ISCAS 2011 - Rio de Janeiro, Brazil
Duration: 15 May 201118 May 2011

Publication series

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

Conference

Conference2011 IEEE International Symposium of Circuits and Systems, ISCAS 2011
Country/TerritoryBrazil
CityRio de Janeiro
Period15/05/1118/05/11

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