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

Goh, Pey Yun, Tan, Shing Chiang, Cheah, Wooi Ping and Lim, Chee Peng 2016, Reducing the complexity of an adaptive radial basis function network with a histogram algorithm, Neural computing and applications, pp. 1-14, doi: 10.1007/s00521-016-2350-4.

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Title Reducing the complexity of an adaptive radial basis function network with a histogram algorithm
Author(s) Goh, Pey Yun
Tan, Shing Chiang
Cheah, Wooi Ping
Lim, Chee PengORCID iD for Lim, Chee Peng
Journal name Neural computing and applications
Start page 1
End page 14
Total pages 14
Publisher Springer Verlag
Place of publication Berlin, Germany
Publication date 2016-05-26
ISSN 0941-0643
Keyword(s) Radial basis function network
Dynamic decay adjustment
Nodes reduction
Summary 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.
Notes In press
Language eng
DOI 10.1007/s00521-016-2350-4
Field of Research 0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, The Natural Computing Applications Forum
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Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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