A Fast Implementation of Radial Basis Function Networks with Application to Time Series Forecasting

Rong Bo Huang; Yiu Ming Cheung

doi:10.1007/978-3-540-45080-1_21

A Fast Implementation of Radial Basis Function Networks with Application to Time Series Forecasting

Rong Bo Huang^*, Yiu Ming Cheung

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

This paper presents a new divide-and-conquer learning approach to radial basis function networks (DCRBF). The DCRBF network is a hybrid system consisting of several sub-RBF networks, each of which takes a sub-input space as its input. Since this system divides a high-dimensional modeling problem into several low-dimensional ones, it can considerably reduce the structural complexity of a RBF network, whereby the net's learning becomes much faster. We have empirically shown its outstanding learning performance on forecasting two real time series as well as synthetic data in comparison with a conventional RBF one.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning
Subtitle of host publication	4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers
Editors	Jiming Liu, Yiu-ming Cheung, Hujun Yin
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	143-150
Number of pages	8
Edition	1st
ISBN (Electronic)	9783540450801
ISBN (Print)	9783540405504
DOIs	https://doi.org/10.1007/978-3-540-45080-1_21
Publication status	Published - 29 Jul 2003
Event	4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003 - , Hong Kong Duration: 21 Mar 2003 → 23 Mar 2003 https://link.springer.com/book/10.1007/b11717

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	2690
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	International Conference on Intelligent Data Engineering and Automated Learning

Conference

Conference	4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003
Country/Territory	Hong Kong
Period	21/03/03 → 23/03/03
Internet address	https://link.springer.com/book/10.1007/b11717

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

User-Defined Keywords

Independent Component Analysis
Principle Component Analysis
Radial Basis Function Network
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10.1007/978-3-540-45080-1_21

Cite this

Huang, R. B., & Cheung, Y. M. (2003). A Fast Implementation of Radial Basis Function Networks with Application to Time Series Forecasting. In J. Liu, Y. Cheung, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers (1st ed., pp. 143-150). (Lecture Notes in Computer Science; Vol. 2690), (International Conference on Intelligent Data Engineering and Automated Learning). Springer. https://doi.org/10.1007/978-3-540-45080-1_21

Huang, Rong Bo ; Cheung, Yiu Ming. / A Fast Implementation of Radial Basis Function Networks with Application to Time Series Forecasting. Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers. editor / Jiming Liu ; Yiu-ming Cheung ; Hujun Yin. 1st. ed. Berlin, Heidelberg : Springer, 2003. pp. 143-150 (Lecture Notes in Computer Science). (International Conference on Intelligent Data Engineering and Automated Learning).

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abstract = "This paper presents a new divide-and-conquer learning approach to radial basis function networks (DCRBF). The DCRBF network is a hybrid system consisting of several sub-RBF networks, each of which takes a sub-input space as its input. Since this system divides a high-dimensional modeling problem into several low-dimensional ones, it can considerably reduce the structural complexity of a RBF network, whereby the net's learning becomes much faster. We have empirically shown its outstanding learning performance on forecasting two real time series as well as synthetic data in comparison with a conventional RBF one.",

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author = "Huang, {Rong Bo} and Cheung, {Yiu Ming}",

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Huang, RB & Cheung, YM 2003, A Fast Implementation of Radial Basis Function Networks with Application to Time Series Forecasting. in J Liu, Y Cheung & H Yin (eds), Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers. 1st edn, Lecture Notes in Computer Science, vol. 2690, International Conference on Intelligent Data Engineering and Automated Learning, Springer, Berlin, Heidelberg, pp. 143-150, 4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003, Hong Kong, 21/03/03. https://doi.org/10.1007/978-3-540-45080-1_21

A Fast Implementation of Radial Basis Function Networks with Application to Time Series Forecasting. / Huang, Rong Bo; Cheung, Yiu Ming.
Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers. ed. / Jiming Liu; Yiu-ming Cheung; Hujun Yin. 1st. ed. Berlin, Heidelberg: Springer, 2003. p. 143-150 (Lecture Notes in Computer Science; Vol. 2690), (International Conference on Intelligent Data Engineering and Automated Learning).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Cheung, Yiu Ming

PY - 2003/7/29

Y1 - 2003/7/29

N2 - This paper presents a new divide-and-conquer learning approach to radial basis function networks (DCRBF). The DCRBF network is a hybrid system consisting of several sub-RBF networks, each of which takes a sub-input space as its input. Since this system divides a high-dimensional modeling problem into several low-dimensional ones, it can considerably reduce the structural complexity of a RBF network, whereby the net's learning becomes much faster. We have empirically shown its outstanding learning performance on forecasting two real time series as well as synthetic data in comparison with a conventional RBF one.

AB - This paper presents a new divide-and-conquer learning approach to radial basis function networks (DCRBF). The DCRBF network is a hybrid system consisting of several sub-RBF networks, each of which takes a sub-input space as its input. Since this system divides a high-dimensional modeling problem into several low-dimensional ones, it can considerably reduce the structural complexity of a RBF network, whereby the net's learning becomes much faster. We have empirically shown its outstanding learning performance on forecasting two real time series as well as synthetic data in comparison with a conventional RBF one.

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KW - Principle Component Analysis

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KW - Hide Unit

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Huang RB, Cheung YM. A Fast Implementation of Radial Basis Function Networks with Application to Time Series Forecasting. In Liu J, Cheung Y, Yin H, editors, Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers. 1st ed. Berlin, Heidelberg: Springer. 2003. p. 143-150. (Lecture Notes in Computer Science). (International Conference on Intelligent Data Engineering and Automated Learning). doi: 10.1007/978-3-540-45080-1_21

A Fast Implementation of Radial Basis Function Networks with Application to Time Series Forecasting

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