Improved chaotic neuro-computer with output-coding for quadratic assignment problems

Koji Mori, Yoshihiko Horio, Kazuyuki Aihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we improve performance of a chaotic neuro-computer in solving quadratic assignment problems (QAPs) by adopting an output-coding which constructs a feasible solution from analog internal-states of neurons at each iteration. Through measurements from the chaotic neuro-computer hardware, we show that we constantly obtain the optimum solution for size-10 QAPs. Furthermore, chaotic search dynamics through chaotic itinerancy is confirmed from time evolutions of a cost function and an energy function. Moreover, we observe internal states of arbitrary three neurons in a network to extract useful information on network dynamics that is effective in solving the QAPs.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages3312-3317
Number of pages6
DOIs
Publication statusPublished - 2005 Dec 1
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 2005 Jul 312005 Aug 4

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume5

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period05/7/3105/8/4

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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