Self-organizing feature map with a momentum term

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
出版社Publ by IEEE
ページ467-470
ページ数4
ISBN(印刷版)0780314212, 9780780314214
出版ステータスPublished - 1993
イベントProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn
継続期間: 1993 10月 251993 10月 29

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
1

Other

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

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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