Rule Learning and Extraction Using a Hybrid Neural Network: A Case Study on Fault Detection and Diagnosis
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, whil...
| Main Authors: | , |
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| Format: | Article |
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2005
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2386/ |
| _version_ | 1848790041796542464 |
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| author | Tan, Shing Chiang Lim, Chee Peng |
| author_facet | Tan, Shing Chiang Lim, Chee Peng |
| author_sort | Tan, Shing Chiang |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:06:19Z |
| format | Article |
| id | mmu-2386 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:06:19Z |
| publishDate | 2005 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-23862011-08-22T07:08:39Z http://shdl.mmu.edu.my/2386/ Rule Learning and Extraction Using a Hybrid Neural Network: A Case Study on Fault Detection and Diagnosis Tan, Shing Chiang Lim, Chee Peng QA75.5-76.95 Electronic computers. Computer science 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. 2005 Article NonPeerReviewed 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 http://dx.doi.org/10.1007/3-540-32400-3_14 doi:10.1007/3-540-32400-3_14 doi:10.1007/3-540-32400-3_14 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Tan, Shing Chiang Lim, Chee Peng Rule Learning and Extraction Using a Hybrid Neural Network: A Case Study on Fault Detection and Diagnosis |
| title | Rule Learning and Extraction Using a Hybrid Neural Network: A Case Study on Fault Detection and Diagnosis |
| title_full | Rule Learning and Extraction Using a Hybrid Neural Network: A Case Study on Fault Detection and Diagnosis |
| title_fullStr | Rule Learning and Extraction Using a Hybrid Neural Network: A Case Study on Fault Detection and Diagnosis |
| title_full_unstemmed | Rule Learning and Extraction Using a Hybrid Neural Network: A Case Study on Fault Detection and Diagnosis |
| title_short | Rule Learning and Extraction Using a Hybrid Neural Network: A Case Study on Fault Detection and Diagnosis |
| title_sort | rule learning and extraction using a hybrid neural network: a case study on fault detection and diagnosis |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2386/ http://shdl.mmu.edu.my/2386/ http://shdl.mmu.edu.my/2386/ |