Parameter analysis for removing the local minima of combinatorial optimization problems by using the inverse function delayed neural network

Yoshihiro Hayakawa, Koji Nakajima

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

Abstract

The Inverse function Delayed (ID) model is a novel neuron model derived from a macroscopic model which is attached to conventional network action. The special characteristic of the ID model is to have the negative resistance effect. Such a negative resistance can actively destabilize undesirable states, and we expect that the ID model can avoid the local minimum problems for solving the combinatorial optimization problem. In computer simulations, we have shown that the ID network can avoid the local minimum problem with a particular combinatorial optimization problem, and we have also shown the existence of an appropriate parameter for finding an optimal solution with high success rate experimentally. In this paper, we theoretically estimate appropriate network parameters to remove all local minimum states.

Original languageEnglish
Title of host publicationAdvances in Neuro-Information Processing - 15th International Conference, ICONIP 2008, Revised Selected Papers
Pages875-882
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 2009 Sep 21
Event15th International Conference on Neuro-Information Processing, ICONIP 2008 - Auckland, New Zealand
Duration: 2008 Nov 252008 Nov 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5506 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Neuro-Information Processing, ICONIP 2008
Country/TerritoryNew Zealand
CityAuckland
Period08/11/2508/11/28

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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