An adaptive fuzzy min-max conflict-resolving classifier

Tan, Shing Chiang, Rao, M. V. C. and Lim, Chee Peng 2006, An adaptive fuzzy min-max conflict-resolving classifier, Advances in soft computing, vol. 34, pp. 65-76, doi: 10.1007/3-540-31662-0_5.

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Title An adaptive fuzzy min-max conflict-resolving classifier
Author(s) Tan, Shing Chiang
Rao, M. V. C.
Lim, Chee PengORCID iD for Lim, Chee Peng
Journal name Advances in soft computing
Volume number 34
Start page 65
End page 76
Total pages 12
Publisher Springer
Place of publication Berlin, Germany
Publication date 2006
ISSN 1615-3871
Keyword(s) adaptive resonance theory
ordering algorithm
dynamic decay adjustment
circulating water system
Summary This 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.
Notes This paper was presented at the 9th Online World Conference on Soft Computing in Industrial Applications 2004.
Language eng
DOI 10.1007/3-540-31662-0_5
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2006, Springer
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