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Power quality analysis using a hybrid model of the fuzzy min-max neural network and clustering tree

Seera, Manjeevan, Lim, Chee Peng, Loo, Chu Kiong and Singh, Harapajan 2016, Power quality analysis using a hybrid model of the fuzzy min-max neural network and clustering tree, IEEE transactions on neural networks and learning systems, vol. 27, no. 12, pp. 2760-2767, doi: 10.1109/TNNLS.2015.2502955.

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Title Power quality analysis using a hybrid model of the fuzzy min-max neural network and clustering tree
Author(s) Seera, Manjeevan
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Loo, Chu Kiong
Singh, Harapajan
Journal name IEEE transactions on neural networks and learning systems
Volume number 27
Issue number 12
Start page 2760
End page 2767
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-12
ISSN 2162-2388
Keyword(s) Clustering algorithm
clustering tree (CT)
fuzzy min–max (FMM) network
power quality monitoring
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
fuzzy min-max (FMM) network
SELF-ORGANIZING MAPS
RULE EXTRACTION
ALGORITHM
DIAGNOSIS
SYSTEM
MIXTURE
Summary A hybrid intelligent model comprising a modified fuzzy min-max (FMM) clustering neural network and a modified clustering tree (CT) is developed. A review of clustering models with rule extraction capabilities is presented. The hybrid FMM-CT model is explained. We first use several benchmark problems to illustrate the cluster evolution patterns from the proposed modifications in FMM. Then, we employ a case study with real data related to power quality monitoring to assess the usefulness of FMM-CT. The results are compared with those from other clustering models. More importantly, we extract explanatory rules from FMM-CT to justify its predictions. The empirical findings indicate the usefulness of the proposed model in tackling data clustering and power quality monitoring problems under different environments.
Language eng
DOI 10.1109/TNNLS.2015.2502955
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089717

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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