An improved fuzzy C-Means algorithm for power load characteristics classification

A simulated annealing and genetic algorithm oriented Fuzzy C-Means (SAGA-FCM) algorithm is used for load classification to improve the accuracy and validity. The traditional Fuzzy C-Means (FCM) algorithm is sensitive to its initial cluster centers, and it is easy to fall into the local optimum. Whil...

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Veröffentlicht in:Dianli Xitong Baohu yu Kongzhi Jg. 40; H. 22; S. 58 - 63
Hauptverfasser: Zhou, Kai-Le, Yang, Shan-Lin
Format: Journal Article
Sprache:Chinesisch
Veröffentlicht: 16.11.2012
Schlagworte:
Algorithms
Classification
Fuzzy
Fuzzy logic
Fuzzy set theory
Genetic algorithms
Searching
Simulated annealing
ISSN:1674-3415
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Zusammenfassung:A simulated annealing and genetic algorithm oriented Fuzzy C-Means (SAGA-FCM) algorithm is used for load classification to improve the accuracy and validity. The traditional Fuzzy C-Means (FCM) algorithm is sensitive to its initial cluster centers, and it is easy to fall into the local optimum. While SAGA-FCM algorithm integrates the strong local search ability of simulated annealing algorithm and the strong global search ability of genetic algorithm to overcome the drawbacks of traditional FCM algorithm. Meanwhile, the hierarchical clustering method, K-Means algorithm and traditional FCM algorithm are also used for power load classification. The comparative analysis from the experimental results shows that SAGA-FCM algorithm is more effective and superior than the other three algorithms.
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ISSN:1674-3415