An adaptive fuzzy min-max conflict-resolving classifier


Tan, , Shing Chiang and Rao, , M. V. C.) and Lim, , Chee Peng (2006) An adaptive fuzzy min-max conflict-resolving classifier. APPLIED SOFT COMPUTING TECHNOLOGIES: THE CHALLENGE OF COMPLEXITY , 34. pp. 65-76. ISSN 1615-3871

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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 leaming 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.

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: 10 Aug 2011 06:56
Last Modified: 10 Aug 2011 06:56


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