Multi-branch structure and its localized property in layered neural networks

Takashi Yamashita, Kotaro Hirasawa, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Neural networks (NNs) can solve only a simple problem if the network size is too compact, on the other hand, if the network size increases, it costs a lot in terms of calculation time. So, we have studied how to construct the network structure with high performances and low costs in space and time. A solution is a multi-branch structure. Conventional NNs uses the single-branch for the connections, while the multi-branch structure has multi-branches between the nodes. In this paper, a new method which enable the multi-branch NNs to have localized property is proposed. It is well known that RBF networks have localized property that makes it possible to approximate functions faster than signioidal NNs. By using the multi-branch structure having localized property, NNs could obtain high performances keeping the lower costs in space and time. Simulation results of function approximations and classification problems illustrated the effectiveness of multi-branch NNs.

Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages1039-1044
Number of pages6
DOIs
Publication statusPublished - 2004 Dec 1
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 2004 Jul 252004 Jul 29

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume2
ISSN (Print)1098-7576

Conference

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

ASJC Scopus subject areas

  • Software

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  • Cite this

    Yamashita, T., Hirasawa, K., & Hu, J. (2004). Multi-branch structure and its localized property in layered neural networks. In 2004 IEEE International Joint Conference on Neural Networks - Proceedings (pp. 1039-1044). (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 2). https://doi.org/10.1109/IJCNN.2004.1380077