Self-organizing feature map with a momentum term

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2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)71-81
Number of pages11
Issue number1
Publication statusPublished - 1996 Jan


  • Momentum term
  • Self-organizing feature map

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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