Self-organizing feature map with a momentum term

研究成果: Article査読

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 the self-organizing feature map algorithm having the momentum term through the following assumptions: (1) The cost function is E(n) = Σ(μ)(n)α(n-μ), where E(μ) is the modified Lyapunov function originally proposed by Ritter and Schulten at the μth learning time and Q is the momentum coefficient. (2) The latest weights are assumed in calculating the cost function E(n). 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
ページ(範囲)71-81
ページ数11
ジャーナルNeurocomputing
10
1
DOI
出版ステータスPublished - 1996 1

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 認知神経科学
  • 人工知能

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