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 |
| 巻 | 8 |
| 発行部数 | 2 |
| 出版物ステータス | Published - 2003 9 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Hardware and Architecture
- Engineering (miscellaneous)
これを引用
An incremental learning of neural network with multiplication units for function approximation. / Li, Dazi; Hirasawa, Kotaro; Furuzuki, Takayuki; Wada, Kiyoshi.
：: Research Reports on Information Science and Electrical Engineering of Kyushu University, 巻 8, 番号 2, 09.2003, p. 135-140.研究成果: Article
}
TY - JOUR
T1 - An incremental learning of neural network with multiplication units for function approximation
AU - Li, Dazi
AU - Hirasawa, Kotaro
AU - Furuzuki, Takayuki
AU - Wada, Kiyoshi
PY - 2003/9
Y1 - 2003/9
N2 - 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.
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.
KW - Function approximation
KW - Higher order neural networks
KW - Multiplication units
KW - Random search
KW - Sigmoidal units
UR - http://www.scopus.com/inward/record.url?scp=0347024904&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0347024904&partnerID=8YFLogxK
M3 - Article
VL - 8
SP - 135
EP - 140
JO - Research Reports on Information Science and Electrical Engineering of Kyushu University
JF - Research Reports on Information Science and Electrical Engineering of Kyushu University
SN - 1342-3819
IS - 2
ER -