Determination of the appropriate node function of NNs by using the cascade-correlation algorithms

Weishui Wan; Kotaro Hirasawa; Junichi Murata; Chunzhi Jin; Jinglu Hu

Determination of the appropriate node function of NNs by using the cascade-correlation algorithms

Weishui Wan^*, Kotaro Hirasawa, Junichi Murata, Chunzhi Jin, Jinglu Hu

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

Abstract

How to determine the appropriate or optimal activation function in the neural networks for a specific learning samples remains open. In this paper the cascade-correlation algorithm which is an efficient constructive algorithm is used after implementation of some kinds of clustering algorithms to produce a modular network structure as a surrogate of activation node functions in the radial basis function (RBF) networks. In this way great improvement on the convergence rate of training algorithms and better approximation are achieved. Simulations with the two-spiral data sets proved the above assertion.

Original language	English
Pages	1177-1182
Number of pages	6
Publication status	Published - 2000 Dec 1
Externally published	Yes
Event	26th Annual Conference of the IEEE Electronics Society IECON 2000 - Nagoya, Japan Duration: 2000 Oct 22 → 2000 Oct 28

Conference

Conference	26th Annual Conference of the IEEE Electronics Society IECON 2000
Country/Territory	Japan
City	Nagoya
Period	00/10/22 → 00/10/28

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Cite this

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abstract = "How to determine the appropriate or optimal activation function in the neural networks for a specific learning samples remains open. In this paper the cascade-correlation algorithm which is an efficient constructive algorithm is used after implementation of some kinds of clustering algorithms to produce a modular network structure as a surrogate of activation node functions in the radial basis function (RBF) networks. In this way great improvement on the convergence rate of training algorithms and better approximation are achieved. Simulations with the two-spiral data sets proved the above assertion.",

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AU - Wan, Weishui

AU - Hirasawa, Kotaro

AU - Murata, Junichi

AU - Jin, Chunzhi

AU - Hu, Jinglu

PY - 2000/12/1

Y1 - 2000/12/1

N2 - How to determine the appropriate or optimal activation function in the neural networks for a specific learning samples remains open. In this paper the cascade-correlation algorithm which is an efficient constructive algorithm is used after implementation of some kinds of clustering algorithms to produce a modular network structure as a surrogate of activation node functions in the radial basis function (RBF) networks. In this way great improvement on the convergence rate of training algorithms and better approximation are achieved. Simulations with the two-spiral data sets proved the above assertion.

AB - How to determine the appropriate or optimal activation function in the neural networks for a specific learning samples remains open. In this paper the cascade-correlation algorithm which is an efficient constructive algorithm is used after implementation of some kinds of clustering algorithms to produce a modular network structure as a surrogate of activation node functions in the radial basis function (RBF) networks. In this way great improvement on the convergence rate of training algorithms and better approximation are achieved. Simulations with the two-spiral data sets proved the above assertion.

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M3 - Paper

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T2 - 26th Annual Conference of the IEEE Electronics Society IECON 2000

Y2 - 22 October 2000 through 28 October 2000

ER -

Determination of the appropriate node function of NNs by using the cascade-correlation algorithms

Abstract

Conference

ASJC Scopus subject areas

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