Size-Reducing RBF Network and Its Application to Control Systems

Shinji Itoh; Junichi Murata; Kotaro Hirasawa

doi:10.1541/ieejeiss.123.338

Size-Reducing RBF Network and Its Application to Control Systems

Shinji Itoh, Junichi Murata, Kotaro Hirasawa

Section of Measurement and Control Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

In this paper, a new approach is proposed to determine the structure of radial basis function (RBF) networks. This approach starts with an enough number of hidden nodes and reduces the number of nodes in the course of learning. The algorithm can be employed in the problems where only the performance index of the network output is given, as well as in the supervised training problems where the desired output values are available. Also, it is applicable to either of classification problems and function approximation problems.

Original language	English
Pages (from-to)	338-344
Number of pages	7
Journal	IEEJ Transactions on Electronics, Information and Systems
Volume	123
Issue number	2
DOIs	https://doi.org/10.1541/ieejeiss.123.338
Publication status	Published - Jan 2003

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Access to Document

10.1541/ieejeiss.123.338

Cite this

@article{599a6f7eb2794c4ebb64e4064ea5d1fc,

title = "Size-Reducing RBF Network and Its Application to Control Systems",

abstract = "In this paper, a new approach is proposed to determine the structure of radial basis function (RBF) networks. This approach starts with an enough number of hidden nodes and reduces the number of nodes in the course of learning. The algorithm can be employed in the problems where only the performance index of the network output is given, as well as in the supervised training problems where the desired output values are available. Also, it is applicable to either of classification problems and function approximation problems.",

author = "Shinji Itoh and Junichi Murata and Kotaro Hirasawa",

year = "2003",

month = jan,

doi = "10.1541/ieejeiss.123.338",

language = "English",

volume = "123",

pages = "338--344",

journal = "IEEJ Transactions on Electronics, Information and Systems",

issn = "0385-4221",

publisher = "The Institute of Electrical Engineers of Japan",

number = "2",

}

TY - JOUR

T1 - Size-Reducing RBF Network and Its Application to Control Systems

AU - Itoh, Shinji

AU - Murata, Junichi

AU - Hirasawa, Kotaro

PY - 2003/1

Y1 - 2003/1

N2 - In this paper, a new approach is proposed to determine the structure of radial basis function (RBF) networks. This approach starts with an enough number of hidden nodes and reduces the number of nodes in the course of learning. The algorithm can be employed in the problems where only the performance index of the network output is given, as well as in the supervised training problems where the desired output values are available. Also, it is applicable to either of classification problems and function approximation problems.

AB - In this paper, a new approach is proposed to determine the structure of radial basis function (RBF) networks. This approach starts with an enough number of hidden nodes and reduces the number of nodes in the course of learning. The algorithm can be employed in the problems where only the performance index of the network output is given, as well as in the supervised training problems where the desired output values are available. Also, it is applicable to either of classification problems and function approximation problems.

UR - http://www.scopus.com/inward/record.url?scp=8344243924&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=8344243924&partnerID=8YFLogxK

U2 - 10.1541/ieejeiss.123.338

DO - 10.1541/ieejeiss.123.338

M3 - Article

AN - SCOPUS:8344243924

SN - 0385-4221

VL - 123

SP - 338

EP - 344

JO - IEEJ Transactions on Electronics, Information and Systems

JF - IEEJ Transactions on Electronics, Information and Systems

IS - 2

ER -

Size-Reducing RBF Network and Its Application to Control Systems

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this