Self-Organizing Map Based on Block Learning(Nonlinear Problems)

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概要

• 論文の詳細を見る

• A block-matching-based self-organizing map (BMSOM) is presented. Finding a winner is carried out for each block, which is a set of neurons arranged in square. The proposed learning process updates the reference vectors of all of the neurons in a winner block. Then, the degrees of vector modifications are mainly controlled by the size (i.e., the number of neurons) of the winner block. To prevent a single cluster with neurons from splitting into some disjointed clusters, the restriction of the block size is imposed in the beginning of learning. At the main stage, this restriction is canceled. In BMSOM learning, the size of a winner block does not always decrease monotonically. The formula used to update the reference vectors is basically uncontrolled by time. Therefore, even if a map is in a nonstationary environment, training the map is probably pursued without interruption to adjust time-controlled parameters such as learning rate. Numerical results demonstrate that the BMSOM makes it possible to improve the plasticity of maps in a nonstationary environment and incremental learning.

• 社団法人電子情報通信学会の論文
• 2005-11-01

著者

• Kamiura N

  Graduate School Of Engineering University Of Hyogo

• Ohtsuka Akitsugu

  Yamato Scale Co. Ltd.

• ISOKAWA Teijiro

  Graduate School of Engineering, University of Hyogo

• OHTSUKA Akitsugu

  Division of Computer Engineering, University of Hyogo

• KAMIURA Naotake

  Division of Computer Engineering, University of Hyogo

• ISOKAWA Teijiro

  Division of Computer Engineering, University of Hyogo

• MATSUI Nobuyuki

  Division of Computer Engineering, University of Hyogo

• Matsui N

  Graduate School Of Engineering University Of Hyogo

• Isokawa Teijiro

  Graduate School Of Engineering University Of Hyogo

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