Citation

Tan, Shing Chiang and Lim, Chee Peng (2005) Rule Learning and Extraction Using a Hybrid Neural Network: A Case Study on Fault Detection and Diagnosis. Soft Computing: Methodologies and Applications. pp. 179-191. ISSN 1615-3871

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Abstract

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.

Item Type:	Article
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 Rosnani Abd Wahab
Date Deposited:	22 Aug 2011 07:08
Last Modified:	22 Aug 2011 07:08
URII:	http://shdl.mmu.edu.my/id/eprint/2386

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