A convenient version of T-S fuzzy model with enhanced performance

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...

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Bibliographic Details
Main Authors: Yu-fei Zhang, Zhi-gang Su, Pei-hong Wang
Format: Conference Proceeding
Language:English
Subjects:
Artificial Bee Colony
automatic fuzzy c-means
Clustering algorithms
Data models
Equations
extracting
Mathematical model
Optimization
Partitioning algorithms
Stochastic processes
T-S fuzzy model
variable length genotypes
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Summary: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.
DOI:10.1109/FSKD.2011.6019748