Reducing the complexity of an adaptive radial basis function network with a histogram algorithm

Pey Yun Goh, Shing Chiang Tan, Wooi Ping Cheah, Chee Peng Lim

Research output: Journal PublicationReview articlepeer-review

1 Citation (Scopus)


In this paper, a constructive training technique known as the dynamic decay adjustment (DDA) algorithm is combined with an information density estimation method to develop a new variant of the radial basis function (RBF) network. The RBF network trained with the DDA algorithm (i.e. RBFNDDA) is able to learn information incrementally by creating new hidden units whenever it is necessary. However, RBFNDDA exhibits a greedy insertion behaviour that absorbs both useful and non-useful information during its learning process, therefore increasing its network complexity unnecessarily. As such, we propose to integrate RBFNDDA with a histogram (HIST) algorithm to reduce the network complexity. The HIST algorithm is used to compute distribution of information in the trained RBFNDDA network. Then, hidden nodes with non-useful information are identified and pruned. The effectiveness of the proposed model, namely RBFNDDA-HIST, is evaluated using a number of benchmark data sets. A performance comparison study between RBFNDDA-HIST and other classification methods is conducted. The proposed RBFNDDA-HIST model is also applied to a real-world condition monitoring problem in a power generation plant. The results are analysed and discussed. The outcome indicates that RBFNDDA-HIST not only can reduce the number of hidden nodes significantly without requiring a long training time but also can produce promising accuracy rates.

Original languageEnglish
Pages (from-to)365-378
Number of pages14
JournalNeural Computing and Applications
Publication statusPublished - 1 Dec 2017
Externally publishedYes


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

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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