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

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)143-150
Number of pages8
JournalLecture Notes in Computer Science
Volume2690
Publication statusPublished - 2004

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'A fast implementation of radial basis function networks with application to time series forecasting'. Together they form a unique fingerprint.

Cite this