Fuzzy Model Identification for Control
Fuzzy Model Identification for Control
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers & Mathematics with Applications
Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Comments on “Dynamical Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN)”
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Alternating cluster estimation: a new tool for clustering and function approximation
IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
MPEG VBR video traffic modeling and classification using fuzzy technique
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
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This paper presents a robust interval type-2 possibilistic C-means (IT2PCM) clustering algorithm which is actually alternating cluster estimation, but membership functions are selected with interval type-2 fuzzy sets by the users. The cluster prototypes are calculated by type reduction combined with defuzzification; consequently they could be directly extracted to generate interval type-2 fuzzy rules that can be used to obtain a first approximation to the interval type-2 fuzzy logic system (IT2FLS). The proposed clustering algorithm is robust to uncertain inliers and outliers, at the same time provides a good initial structure of IT2FLS for further tuning in a subsequent process. Excellent simulation results are obtained for the problem of classification and forecasting.