Citation

Goh, Pey Yun and Tan, Shing Chiang and 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. ISSN 0941-0643

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Radial basis function network,Dynamic decay adjustment, Pruning, Histogram, Nodes reduction
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	15 Feb 2017 05:17
Last Modified:	15 Feb 2017 05:17
URII:	http://shdl.mmu.edu.my/id/eprint/6430

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