Fuzzy ARTMAP dynamic decay adjustment: An improved fuzzy ARTMAP model with a conflict resolving facility
This paper presents a hybrid neural network classifier of fuzzy ARTMAP (FAM) and the dynamic decay adjustment (DDA) algorithm. The proposed FAMDDA model is a conflict-resolving classifier that can perform stable and incremental learning while settling overlapping of hyper-rectangular prototypes of d...
| Main Authors: | , , |
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| Format: | Article |
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ELSEVIER SCIENCE BV
2008
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2780/ |
| Summary: | This paper presents a hybrid neural network classifier of fuzzy ARTMAP (FAM) and the dynamic decay adjustment (DDA) algorithm. The proposed FAMDDA model is a conflict-resolving classifier that can perform stable and incremental learning while settling overlapping of hyper-rectangular prototypes of different classes in minimizing misclassification rates. The performance of FAMDDA is evaluated using a number of benchmark data sets. The results are analyzed and compared with those from FAM and a number of machine learning classifiers. The outcomes show that FAMDDA has a better generalization capability than FAM, and its performance is comparable with those from other classifiers. The effectiveness of FAMDDA is also demonstrated in an application pertaining to condition monitoring of a circulating water system in a power generation station. Implications on the effectiveness of FAMDDA from the application point of view are discussed. (C) 2007 Elsevier B. V. All rights reserved. |
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