Study of multi-branch structure of Universal Learning Networks

Shingo Mabu, Kaoru Shimada, Kotaro Hirasawa, Jinglu Hu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)393-403
Number of pages11
JournalApplied Soft Computing Journal
Volume9
Issue number1
DOIs
Publication statusPublished - 2009 Jan 1

Keywords

  • Multi-branch structure
  • Time-delayed recurrent networks
  • Universal Learning Networks

ASJC Scopus subject areas

  • Software

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