An incremental learning of neural network with multiplication units for function approximation

Dazi Li, Kotaro Hirasawa*, Jinglu Hu, Kiyoshi Wada

*この研究の対応する著者

研究成果: Article査読

抄録

This paper presents a constructive neural network with sigmoidal units and multiplication units, which can uniformly approximate any continuous function on a compact set in multi-dimensional input space. This network provides a more efficient and regular architecture compared to existing higher-order feedforward networks while maintaining their fast learning property. Proposed network provides a natural mechanism for incremental network growth. Simulation results on function approximation problem are given to highlight the capability of the proposed network. In particular, self-organizing process with RasID learning algorithm developed for the network is shown to yield smooth generation and steady learning.

本文言語English
ページ(範囲)135-140
ページ数6
ジャーナルResearch Reports on Information Science and Electrical Engineering of Kyushu University
8
2
出版ステータスPublished - 2003 9 1

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 電子工学および電気工学

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