Self-organizing feature map with a momentum term

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

2 Citations (Scopus)

Abstract

The objectives of this paper are to derive a momentum term in the Kohonen's self-organizing feature map algorithm theoretically and to show the effectiveness of the term by computer simulations. We will derive a self-organizing feature map algorithm having the momentum term through the following assumptions: 1) The cost function is En = Σμn αn-μ Eμ, where Eμ is the modified Lyapunov function originally proposed by Ritter and Schulten at the μ th learning time and α is the momentum coefficient. 2) The latest weights are assumed in calculating the cost function En. According to our simulations, it has shown that the momentum term in the self-organizing feature map can considerably contribute to the acceleration of the convergence.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages467-470
Number of pages4
ISBN (Print)0780314212, 9780780314214
Publication statusPublished - 1993 Dec 1
EventProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn
Duration: 1993 Oct 251993 Oct 29

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume1

Other

OtherProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)
CityNagoya, Jpn
Period93/10/2593/10/29

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Hagiwara, M. (1993). Self-organizing feature map with a momentum term. In Proceedings of the International Joint Conference on Neural Networks (pp. 467-470). (Proceedings of the International Joint Conference on Neural Networks; Vol. 1). Publ by IEEE.