A time-varying recurrent neural system for convex programming

Jun Wang

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

10 Citations (Scopus)

Abstract

The asymptotic stability of a recurrent neural network with monotonically time-varying penalty parameter for optimization is theoretically justified. The conditions of feasibility of solutions generated by the recurrent neural networks are characterized. The conditions of optimality of solutions to convex programming problems generated by the recurrent neural networks are characterized. The design methodology of the operating characteristics of the recurrent neural networks are presented by illustrative examples.

Original languageEnglish
Title of host publicationProceedings. IJCNN-91-Seattle
Subtitle of host publicationInternational Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Pages147-152
Number of pages6
ISBN (Print)0780301641
Publication statusPublished - 1991
Externally publishedYes
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: 8 Jul 199112 Jul 1991

Publication series

NameProceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks

Conference

ConferenceInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA
Period8/07/9112/07/91

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