License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.08041.3
URN: urn:nbn:de:0030-drops-14202
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2008/1420/
Go to the corresponding Portal

Tino, Peter

Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks

pdf-format:
08041.TinoPeter.Paper.1420.pdf (0.3 MB)

Abstract

Optimization dynamics using self-organizing neural networks (SONN) driven by softmax
weight renormalization has been shown to be capable of intermittent search for high-quality solutions in assignment optimization problems. However, the search is sensitive to
temperature setting in the softmax renormalization step. The powerful search occurs only at the critical temperature that depends on the problem size.
So far the critical temperatures have been determined only by tedious trial-and-error numerical simulations. We offer a rigorous analysis of the search performed by SONN
and derive analytical approximations to the critical temperatures.
We demonstrate on a set of N-queens problems for a wide range of problem sizes N that
the analytically determined critical temperatures predict the optimal working temperatures for SONN intermittent search very well.


BibTeX - Entry

@InProceedings{tino:DagSemProc.08041.3,
  author =	{Tino, Peter},
  title =	{{Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks}},
  booktitle =	{Recurrent Neural Networks- Models, Capacities, and Applications},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8041},
  editor =	{Luc De Raedt and Barbara Hammer and Pascal Hitzler and Wolfgang Maass},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2008/1420},
  URN =		{urn:nbn:de:0030-drops-14202},
  doi =		{10.4230/DagSemProc.08041.3},
  annote =	{Keywords: Recurrent self-organizing maps, symmetry breaking bifurcation, N-queens}
}

Keywords: Recurrent self-organizing maps, symmetry breaking bifurcation, N-queens
Collection: 08041 - Recurrent Neural Networks- Models, Capacities, and Applications
Issue Date: 2008
Date of publication: 15.04.2008

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI