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Evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis

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Abstract

In this paper, two mutation-based evolving artificial neural networks, which are based on the Fuzzy ARTMAP (FAM) network and evolutionary programming, are proposed. The networks utilize the knowledge base extracted from a set of data to perform search and adaptation. The performances of the two networks are assessed using benchmark problems, with the results analyzed and discussed. The effects of the network parameters are evaluated through a parametric study. The applicability of the networks is also demonstrated using a real fault detection and diagnosis task in a power generation plant. The experimental results consistently indicate the usefulness of the proposed evolutionary FAM-based networks in yielding good classification performances with parsimonious network structures.

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Authors and Affiliations

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

    Shing Chiang Tan

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

    Chee Peng Lim

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  1. Shing Chiang Tan
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  2. Chee Peng Lim
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Correspondence to Shing Chiang Tan.

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Tan, S.C., Lim, C.P. Evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis. Neural Process Lett 31, 219–242 (2010). https://doi.org/10.1007/s11063-010-9135-z

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