A modified fuzzy min-max neural network for data clustering and its application to power quality monitoring
Data(s) |
01/01/2015
|
---|---|
Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Elsevier |
Relação |
http://dro.deakin.edu.au/eserv/DU:30074920/lim-modifiedfuzzy-2015.pdf http://www.dx.doi.org/10.1016/j.asoc.2014.09.050 |
Direitos |
2015, Elsevier |
Palavras-Chave | #Benchmark study #Clustering #Fuzzy min-max neural network #Power quality monitoring #Science & Technology #Technology #Computer Science, Artificial Intelligence #Computer Science, Interdisciplinary Applications #Computer Science #K-MEANS #IMAGE SEGMENTATION #ALGORITHM #CLASSIFICATION #INFORMATION #CENTROIDS #PARAMETER |
Tipo |
Journal Article |