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A hybrid intelligent system and its application to fault detection and diagnosis
This paper proposes a hybrid system that integrates the SOM (Self Organizing Map) neural network, the kMER (kernel-based Maximum Entropy learning Rule) algorithm and the Probabilistic Neural Network (PNN) for data visualization and classification. The rationales of this hybrid SOM-kMER-PNN model are explained, and the applicability of the proposed model is demonstrated using two benchmark data sets and a real-world application to fault detection and diagnosis. The outcomes show that the hybrid system is able to achieve comparable classification rates when compared to those from a number of existing classifiers and, at the same time, to produce meaningful visualization of the data sets.
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Journal
Advances in soft computingVolume
36Pagination
165 - 175Publisher
SpringerLocation
Berlin , GermanyPublisher DOI
ISSN
1615-3871Language
engNotes
This paper was presented at the 10th Online World Conference on Soft Computing in Industrial Applications 2005Publication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2006, SpringerUsage metrics
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