An online pruning strategy for supervised ARTMAP-based neural networks |
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Authors: | Shing Chiang Tan M. V. C. Rao Chee Peng Lim |
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Affiliation: | (1) Faculty of Information Science and Technology, Multimedia University, Melaka Campus, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Malacca, Malaysia;(2) Faculty of Engineering and Technology, Multimedia University, Melaka Campus, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Malacca, Malaysia;(3) School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia |
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Abstract: | Identifying an appropriate architecture of an artificial neural network (ANN) for a given task is important because learning
and generalisation of an ANN is affected by its structure. In this paper, an online pruning strategy is proposed to participate
in the learning process of two constructive networks, i.e. fuzzy ARTMAP (FAM) and fuzzy ARTMAP with dynamic decay adjustment
(FAMDDA), and the resulting hybrid networks are called FAM/FAMDDA with temporary nodes (i.e. FAM-T and FAMDDA-T, respectively).
FAM-T and FAMDDA-T possess a capability of reducing the network complexity online by removing unrepresentative neurons. The
performances of FAM-T and FAMDDA-T are evaluated and compared with those of FAM and FAMDDA using a total of 13 benchmark data
sets. To demonstrate the applicability of FAM-T and FAMDDA-T, a real fault detection and diagnosis task in a power plant is
tested. The results from both benchmark studies and real-world application show that FAMDDA-T and FAM-T are able to yield
satisfactory classification performances, with the advantage of having parsimonious network structures. |
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Keywords: | Fuzzy ARTMAP Dynamic decay adjustment Online pruning |
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