A Hybrid Forward Algorithm for RBF Neural Network Construction

Jian Xun Peng, Kang Li, D.S. Huang

Research output: Contribution to journalArticlepeer-review

79 Citations (Scopus)


This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness
Original languageEnglish
Pages (from-to)1439-1451
Number of pages13
JournalIEEE Transactions on Neural Networks
Volume17 (6)
Issue number6
Publication statusPublished - Nov 2006

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture


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