A nodes reduction procedure for RBFNDDA through histogram

Pey Yun Goh, Shing Chiang Tan, Wooi Ping Cheah

Research output: Journal PublicationArticlepeer-review


This paper presents a two-stage learning algorithm to reduce the hidden nodes of a radial basis function network (RBFN). The first stage involves the construction of an RBFN using the dynamic decay adjustment (DDA) and the second stage involves the use of a modified histogram algorithm (HIST) to reduce hidden neurons. DDA enables the RBFN to perform constructive learning without pre-defining the number of hidden nodes. The learning process of DDA is fast but it tends to generate a large network architecture as a result of its greedy insertion behavior. Therefore, an RBFNDDA-HIST is proposed to reduce the nodes. The proposed RBFNDDA-HIST is tested with three benchmark medical datasets. The experimental results show that the accuracy of the RBFNDDA-HIST is compatible with to that of RBFNDDA but with less number of nodes. This proposed network is favorable in a real environment because the computation cost can be reduced.

Original languageEnglish
Pages (from-to)127-134
Number of pages8
JournalLecture Notes in Computer Science
Publication statusPublished - 2014
Externally publishedYes


  • Dynamic decay adjustment
  • Histogram
  • Nodes reduction
  • Radial basis network

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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