A Divide-and-Conquer Based Radial Basis Function Network with Application to Recurrent Function Modelling

Rong Bo Huang*, Yiu Ming Cheung, Lap Tak Law

*Corresponding author for this work

Research output: Contribution to conferenceConference paperpeer-review

1 Citation (Scopus)

Abstract

In this paper, a new architecture of divide-and-conquer based radial basis function network (DCRBF) and its learning algorithm are presented. The DCRBF network is a hybrid system consisting of several sub-RBF networks, each of which individually takes a sub-input space as its input. The output of this new architecture is a linear combination of the sub-networks' outputs with the coefficients tuned together with each sub-network system parameters. Since this system divides a high-dimensional modelling problem into several low-dimensional ones, it can considerably reduce the structural complexity of a RBF network, whereby the net's learning speed as a whole is significantly improved with the comparable generalization ability. We apply DCRBF to model a recurrent version of RBF networks. The experimental results have shown its outstanding performance.

Original languageEnglish
Pages513-516
Number of pages4
Publication statusPublished - 2003
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: 20 Jul 200324 Jul 2003

Conference

ConferenceInternational Joint Conference on Neural Networks 2003
Country/TerritoryUnited States
CityPortland, OR
Period20/07/0324/07/03

Scopus Subject Areas

  • Software
  • Artificial Intelligence

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