不完全観測環境下でのニューロン構造の自己組織化

カテゴリ: 部門大会

論文No: MC4-8

グループ名: 【C】平成15年電気学会電子・情報・システム部門大会講演論文集

発行日: 2003/08/29

タイトル(英語): A Self-Organizing Neural Structure using Incomplete Environmental Observation

著者名: 酒井 正夫(東北大学),本間経康 (東北大学),阿部 健一(東北大学)

著者名(英語): Masao Sakai(Tohoku University),Noriyasu Homma(Tohoku University),Kenichi Abe(Tohoku University)

キーワード: ニューラルネットワーク|自己組織化|概念形成|学習|ヘッブ則 |neural networks|self-organizing|concept formation|learning|Hebbian rule

要約(日本語): We propose a self-organizing neural structure with dynamic and spatial changing weights for concept formation.

An essential core of this self-organization is based on an extended Hebbian rule for the spatial changing and a self-organizing learning with incomplete information for the dynamic changing.

A concept formation problem requires the neural network to acquire the complete information structure of a concept using an incomplete observation of the concept.

The connection structure of self-organizing network can store with the information structure by using the two rules.

The Hebbian rule can create a necessary connection corresponding to the blind complete information.

On the other hand, self-organization can delete unnecessary connections.

Finally concept formation ability of the proposed neural network is proven under some conditions.

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