A hybrid intelligent system and its application to fault detection and diagnosis

Teh, Chee Siong and Lim, Chee Peng 2006, A hybrid intelligent system and its application to fault detection and diagnosis, Advances in soft computing, vol. 36, pp. 165-175, doi: 10.1007/978-3-540-36266-1_16.

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Title A hybrid intelligent system and its application to fault detection and diagnosis
Author(s) Teh, Chee Siong
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Journal name Advances in soft computing
Volume number 36
Start page 165
End page 175
Total pages 11
Publisher Springer
Place of publication Berlin , Germany
Publication date 2006
ISSN 1615-3871
Summary 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.
Notes This paper was presented at the 10th Online World Conference on Soft Computing in Industrial Applications 2005
Language eng
DOI 10.1007/978-3-540-36266-1_16
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048090

Document type: Journal Article
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