Learning rule for TSK fuzzy logic systems using interval type-2 fuzzy subtractive clustering

Authors:
Binh Huy Pham;Hai Trung Ha;Long Thanh Ngo
Affiliations:
Department of Information Systems, Faculty of Information Technology, Le Quy Don Technical University, Hanoi, Vietnam;Department of Information Systems, Faculty of Information Technology, Le Quy Don Technical University, Hanoi, Vietnam;Department of Information Systems, Faculty of Information Technology, Le Quy Don Technical University, Hanoi, Vietnam
Venue:
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Year:
2012

Citing 8
Cited 0

Structure identification of fuzzy model

Fuzzy Sets and Systems
Subtractive clustering based modeling of job sequencing with parametric search

Fuzzy Sets and Systems - Data analysis
Higher order fuzzy system identification using subtractive clustering

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Tool Condition Monitoring Using the TSK Fuzzy Approach Based on Subtractive Clustering Method

IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Uncertainty prediction for tool wear condition using type-2 TSK fuzzy approach

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Type-2 fuzzy sets made simple

IEEE Transactions on Fuzzy Systems
Interval Type-2 Fuzzy Logic Systems Made Simple

IEEE Transactions on Fuzzy Systems
Approach to image segmentation based on interval type-2 fuzzy subtractive clustering

ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper deals with an approach to model TSK fuzzy logic systems (FLS), especially interval type-2 TSK FLS, using interval type-2 fuzzy subtractive clustering (IT2-SC). The IT2-SC algorithm is combined with least square estimation (LSE) algorithms to pre-identify a type-1 FLS form from input/output data. Then, an interval type-2 TSK FLS can be obtained by considering the membership functions of its existed type-1 counterpart as primary membership functions and assigning uncertainty to cluster centroids, standard deviation of Gaussian membership functions and consequence parameters. Results is shown in comparison with the approach based on type-1 subtractive clustering algorithm.