Study of multi-branch structure of Universal Learning Networks

Shingo Mabu, Kaoru Shimada, Kotaro Hirasawa*, Jinglu Hu

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

In this paper, multi-branch structure of Universal Learning Networks (ULNs) is studied to verify its effectiveness for obtaining compact models, which have neurons connected with other neurons using more than two branches having nonlinear functions. Multi-branch structure has been proved to have higher representation/generalization ability and lower computational cost than conventional neural networks because of the nonlinear function of the multi-branches and the reduction of the number of neurons to be used. In addition, learning of delay elements of multi-branch ULNs has improved their potential to build up a compact dynamical model with higher performances and lower computational cost when applied for identifying dynamical systems.

本文言語English
ページ(範囲)393-403
ページ数11
ジャーナルApplied Soft Computing Journal
9
1
DOI
出版ステータスPublished - 2009 1 1

ASJC Scopus subject areas

  • ソフトウェア

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