A fast adaptive algorithm for hopfield neural network

X. C. Zhao, X. G. Wang, Z. Tang, H. Tamura, M. Ishii, G. Z. Zeng

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

1 Scopus citations

Abstract

This paper presents a gradient-based algorithm to speed up the convergence of the Hopfield neural network. To archive this, we introduce an individual step size η, which is adapted according to the gradient information. The algorithm is applied to some benchmark problems, extensive simulations are performed and its effectiveness is confirmed.

Original languageEnglish
Title of host publicationSICE 2003 Annual Conference, SICE 2003
PublisherSociety of Instrument and Control Engineers (SICE)
Pages638-642
Number of pages5
ISBN (Electronic)0780383524
StatePublished - 2003
EventSICE 2003 Annual Conference, SICE 2003 - Fukui, Japan
Duration: 2003/08/042003/08/06

Publication series

NameProceedings of the SICE Annual Conference
Volume1

Conference

ConferenceSICE 2003 Annual Conference, SICE 2003
Country/TerritoryJapan
CityFukui
Period2003/08/042003/08/06

Keywords

  • Combinatorial optimization
  • Gradient-based
  • Hopfield neural network
  • Traveling salesman problem

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

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