Rule learning and extraction using a hybrid neural network : a case study on fault detection and diagnosis

Tan, Shing Chiang and Lim, Chee Peng 2003, Rule learning and extraction using a hybrid neural network : a case study on fault detection and diagnosis, Advances in soft computing, vol. 32, pp. 179-191.

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Title Rule learning and extraction using a hybrid neural network : a case study on fault detection and diagnosis
Author(s) Tan, Shing Chiang
Lim, Chee Peng
Journal name Advances in soft computing
Volume number 32
Start page 179
End page 191
Total pages 13
Publisher Springer
Place of publication Berlin, Germany
Publication date 2003
ISSN 1615-3871
Summary A hybrid network, based on the integration of Fuzzy ARTMAP (FAM) and the Rectangular Basis Function Network (RecBFN), is proposed for rule learning and extraction problems. The underlying idea for such integration is that FAM operates as a classifier to cluster data samples based on similarity, while the RecBFN acts as a “compressor” to extract and refine knowledge learned by the trained FAM network. The hybrid network is capable of classifying data samples incrementally as well as of acquiring rules directly from data samples for explaining its predictions. To evaluate the effectiveness of the hybrid network, it is applied to a fault detection and diagnosis task by using a set of real sensor data collected from a Circulating Water (CW) system in a power generation plant. The rules extracted from the network are analyzed and discussed, and are found to be in agreement with experts’ opinions used in maintaining the CW system.
Notes This paper was presented at the 8th Online World Conference on Soft Computing in Industrial Applications 2003.
Language eng
Field of Research 109999 Technology not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30050268

Document type: Journal Article
Collection: Institute for Frontier Materials
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