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

Takashi Yamashita, Kotaro Hirasawa, Jinglu Hu

研究成果: Conference contribution

4 引用 (Scopus)

抜粋

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.

元の言語English
ホスト出版物のタイトル2004 IEEE International Joint Conference on Neural Networks - Proceedings
ページ1039-1044
ページ数6
DOI
出版物ステータス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(印刷物)1098-7576

Conference

Conference2004 IEEE International Joint Conference on Neural Networks - Proceedings
Hungary
Budapest
期間04/7/2504/7/29

ASJC Scopus subject areas

  • Software

フィンガープリント Multi-branch structure and its localized property in layered neural networks' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

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