Self-organized function localization neural network

Takafumi Sasakawa, Jinglu Hu, Kotaro Hirasawa

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

This paper presents a self-organizing function localization neural network (FLNN) inspired by Hebb's cell assembly theory about how the brain worked. The proposed self-organizing FLNN consists of two parts: main part and control part. The main part is an ordinary 3-layered feedforward neural network, but each hidden neuron contains a signal from the control part, controlling its firing strength. The control part consists of a SOM network whose outputs are associated with the hidden neurons of the main part. Trained with an unsupervised learning, SOM control part extracts structural features of input-output spaces and controls the firing strength of hidden neurons in the main part. Such self-organizing FLNN realizes capabilities of function localization and learning. Numerical simulations show that the self-organizing FLNN has superior performance than an ordinary neural network.

本文言語English
ホスト出版物のタイトル2004 IEEE International Joint Conference on Neural Networks - Proceedings
ページ1463-1468
ページ数6
DOI
出版ステータスPublished - 2004 12 1
イベント2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
継続期間: 2004 7 252004 7 29

出版物シリーズ

名前IEEE International Conference on Neural Networks - Conference Proceedings
2
ISSN(印刷版)1098-7576

Conference

Conference2004 IEEE International Joint Conference on Neural Networks - Proceedings
CountryHungary
CityBudapest
Period04/7/2504/7/29

ASJC Scopus subject areas

  • Software

フィンガープリント 「Self-organized function localization neural network」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル