Multibranch structure and its localized property in layered neural networks

Takashi Yamashita*, Kotaro Hirasawa, Takayuki Furuzuki


研究成果: Article査読


Neural networks (NNs) can solve only simple problems if the network size is too small, but increasing the network size is costly in terms of memory space and calculation time. Thus, we have studied how to construct a network structure with high performance and low cost in space and time. One solution is a multibranch structure. Conventional NNs use the single-branch structure for connections, while the multibranch structure has multiple branches between nodes. In this paper, a new method which enables the multibranch NNs to have the localized property is proposed. It is well known that RBF networks have the localized property, which makes it possible to approximate functions faster than sigmoidal NNs. By using the multibranch structure having the localized property of RBF networks, NNs can obtain superior performance while maintaining lower costs in space and time. Simulation results of function approximations and a classification problem are presented to illustrate the effectiveness of multibranch NNs.

ジャーナルElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
出版ステータスPublished - 2008 1月 15

ASJC Scopus subject areas

  • エネルギー工学および電力技術
  • 電子工学および電気工学


「Multibranch structure and its localized property in layered neural networks」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。