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An online pruning strategy for supervised ARTMAP-based neural networks

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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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Acknowledgment

The authors are grateful to Prai Power Station, Tenaga Nasional Generation Sdn. Bhd. for providing the database used in this research work.

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Authors and Affiliations

  1. Faculty of Information Science and Technology, Multimedia University, Melaka Campus, Jalan Ayer Keroh Lama, Bukit Beruang, 75450, Malacca, Malaysia

    Shing Chiang Tan

  2. Faculty of Engineering and Technology, Multimedia University, Melaka Campus, Jalan Ayer Keroh Lama, Bukit Beruang, 75450, Malacca, Malaysia

    M. V. C. Rao

  3. School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia

    Chee Peng Lim

Authors
  1. Shing Chiang Tan
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  2. M. V. C. Rao
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  3. Chee Peng Lim
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Correspondence to Shing Chiang Tan.

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Tan, S.C., Rao, M.V.C. & Lim, C.P. An online pruning strategy for supervised ARTMAP-based neural networks. Neural Comput & Applic 18, 387–395 (2009). https://doi.org/10.1007/s00521-008-0191-5

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  • DOI: https://doi.org/10.1007/s00521-008-0191-5

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