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A modified fuzzy min-max neural network for data clustering and its application to power quality monitoring

Seera, Manjeevan, Lim, Chee Peng, Loo, Chu Kiong and Singh, Harapajan 2015, A modified fuzzy min-max neural network for data clustering and its application to power quality monitoring, Applied Soft Computing, vol. 28, pp. 19-29, doi: 10.1016/j.asoc.2014.09.050.

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Title A modified fuzzy min-max neural network for data clustering and its application to power quality monitoring
Author(s) Seera, Manjeevan
Lim, Chee PengORCID iD for Lim, Chee Peng
Loo, Chu Kiong
Singh, Harapajan
Journal name Applied Soft Computing
Volume number 28
Start page 19
End page 29
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015
ISSN 1568-4946
Keyword(s) Benchmark study
Fuzzy min-max neural network
Power quality monitoring
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Computer Science
Summary When no prior knowledge is available, clustering is a useful technique for categorizing data into meaningful groups or clusters. In this paper, a modified fuzzy min-max (MFMM) clustering neural network is proposed. Its efficacy for tackling power quality monitoring tasks is demonstrated. A literature review on various clustering techniques is first presented. To evaluate the proposed MFMM model, a performance comparison study using benchmark data sets pertaining to clustering problems is conducted. The results obtained are comparable with those reported in the literature. Then, a real-world case study on power quality monitoring tasks is performed. The results are compared with those from the fuzzy c-means and k-means clustering methods. The experimental outcome positively indicates the potential of MFMM in undertaking data clustering tasks and its applicability to the power systems domain.
Language eng
DOI 10.1016/j.asoc.2014.09.050
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
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Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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Created: Mon, 10 Aug 2015, 08:32:43 EST

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