1. Power Quality Analysis Using a Hybrid Model of the Fuzzy Min–Max Neural Network and Clustering Tree.
- Author
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Seera, Manjeevan, Lim, Chee Peng, Loo, Chu Kiong, and Singh, Harapajan
- Subjects
- *
ALGORITHMS , *FUZZY neural networks , *RULE extraction (Machine learning) - Abstract
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. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
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