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An adaptive fuzzy min-max conflict-resolving classifier
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posted on 2006-01-01, 00:00 authored by S Tan, M Rao, Chee Peng LimChee Peng LimThis paper describes a novel adaptive network, which agglomerates a procedure based on the fuzzy min-max clustering method, a supervised ART (Adaptive Resonance Theory) neural network, and a constructive conflict-resolving algorithm, for pattern classification. The proposed classifier is a fusion of the ordering algorithm, Fuzzy ARTMAP (FAM) and the Dynamic Decay Adjustment (DDA) algorithm. The network, called Ordered FAMDDA, inherits the benefits of the trio, viz . an ability to identify a fixed order of training pattern presentation for good generalisation; stable and incrementally learning architecture; and dynamic width adjustment of the weights of hidden nodes of conflicting classes. Classification performance of the Ordered FAMDDA is assessed using two benchmark datasets. The performances are analysed and compared with those from FAM and Ordered FAM. The results indicate that the Ordered FAMDDA classifier performs at least as good as the mentioned networks. The proposed Ordered FAMDDA network is then applied to a condition monitoring problem in a power generation station. The process under scrutiny is the Circulating Water (CW) system, with prime attention to condition monitoring of the heat transfer efficiency of the condensers. The results and their implications are analysed and discussed.
History
Journal
Advances in soft computingVolume
34Pagination
65 - 76Publisher
SpringerLocation
Berlin, GermanyPublisher DOI
ISSN
1615-3871Language
engNotes
This paper was presented at the 9th Online World Conference on Soft Computing in Industrial Applications 2004.Publication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2006, SpringerUsage metrics
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