DocumentCode :
3308449
Title :
A convenient version of T-S fuzzy model with enhanced performance
Author :
Yu-Fei Zhang ; Zhi-gang Su ; Pei-hong Wang
Author_Institution :
Sch. of Energy & Environ., Southeast Univ., Nanjing, China
Volume :
2
fYear :
2011
fDate :
26-28 July 2011
Firstpage :
1074
Lastpage :
1079
Abstract :
This paper proposes a methodology for automatically extracting a convenient version of T-S fuzzy models from data using a novel clustering technique, called variable string length Artificial Bee Colony (ABC) algorithm based fuzzy c-means clustering approach (VABC-FCM). In this methodology, the rule number of the T-S model is automatically determined by the VABC-FCM, without knowing the rule number as a prior. In addition, the fuzzy partition matrix of the VABC-FCM is directly applied to identify the T-S fuzzy model, which brings convenience to the T-S fuzzy model construction. Experiments show that the proposed methodology presents a convenient version of T-S fuzzy model with enhanced performance.
Keywords :
fuzzy set theory; optimisation; pattern clustering; T-S fuzzy model; VABC-FCM methodology; artificial bee colony; fuzzy c-means clustering; fuzzy partition matrix; rule number; variable string length; Clustering algorithms; Data models; Equations; Mathematical model; Optimization; Partitioning algorithms; Stochastic processes; Artificial Bee Colony; T-S fuzzy model; automatic fuzzy c-means; extracting; variable length genotypes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
Type :
conf
DOI :
10.1109/FSKD.2011.6019748
Filename :
6019748
Link To Document :
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