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

Dazi Li, Kotaro Hirasawa, Takayuki Furuzuki, Kiyoshi Wada

研究成果: Article

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.

元の言語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
発行部数2
出版物ステータスPublished - 2003 9

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Neural networks
Learning algorithms

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Hardware and Architecture
  • Engineering (miscellaneous)

これを引用

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AB - 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.

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KW - Sigmoidal units

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