Paper Info
Title
A novel iterative fuzzy identification via OCA and its application to electrical distribution problem
Abstract
A novel iterative fuzzy identification via Objective Cluster Analysis is proposed in this paper. The Objective Cluster Analysis algorithm is introduced and enhanced using the relative dissimilarity measure and the new consistency criterion for improving the robustness and the compactness of clustering. Then the Fuzzy c - Means clustering algorithm and the Stable Kalman Filter algorithm are respectively incorporated to identify the premise and the consequence parameters. For making the local fuzzy partitions more satisfying, the iterative fuzzy identification procedure is presented with the covering criterion to acquire the supplementary fuzzy rule prototypes. The developed approach is then applied to a case study of electrical distribution problem for relating the relationship between the village characteristics and the length of low voltage line. The results demonstrate that our method is effective.
Year
DOI
Venue
2011
10.1109/FSKD.2011.6019575
FSKD
Keywords
Field
DocType
fuzzy clustering,fuzzy set theory,kalman filter,pattern clustering,electrical distribution problem,kalman filters,fuzzy rule,fuzzy identification,oca,electrical distribution,objective cluster analysis,consistency criterion,fuzzy c means clustering algorithm,power distribution lines,iterative fuzzy identification,data integrity,iterative methods,low voltage,prototypes,electricity distribution,satisfiability,cluster analysis,clustering algorithms,algorithm design,accuracy,algorithm design and analysis
Fuzzy clustering,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy set,Artificial intelligence,Fuzzy number,Mathematical optimization,Defuzzification,Pattern recognition,Fuzzy logic,Machine learning,Fuzzy rule
Conference
Volume
Issue
ISBN
1
null
978-1-61284-180-9
Citations 
PageRank 
References 
0
0.34
7
Authors
2
Name
Order
Citations
PageRank
Chaofang Hu1617.16
Na Wang2711.10