Performance optimization of function localization neural network by using reinforcement learning

Takafumi Sasakawa*, Jinglu Hu, Kotaro Hirasawa

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

According to Hebb's cell assembly theory, the brain has the capability of function localization. On the other hand, it is suggested that the brain has three different learning paradigms: supervised, unsupervised and reinforcement learning. Inspired by the above knowledge of brain, we present a selforganizing function localization neural network (FLNN), that contains supervised, unsupervised and reinforcement learning paradigms. In this paper, we concentrate our discussion mainly on applying a simplified reinforcement learning called evaluative feedback to optimization of the self-organizing FLNN. Numerical simulations show that the self-organizing FLNN has superior performance to an ordinary artificial neural network (ANN).

本文言語English
ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
ページ1314-1319
ページ数6
DOI
出版ステータスPublished - 2005 12月 1
イベントInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
継続期間: 2005 7月 312005 8月 4

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
2

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
国/地域Canada
CityMontreal, QC
Period05/7/3105/8/4

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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