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

Manjeevan Seera, Chee Peng Lim, Chu Kiong Loo, Harapajan Singh

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17 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2760-2767
Number of pages8
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number12
Publication statusPublished - Dec 2016
Externally publishedYes


  • Clustering algorithm
  • clustering tree (CT)
  • fuzzy min-max (FMM) network
  • power quality monitoring

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