Knowledge Representation Based On Interval Type-2 CFCM Clustering

Abstract—This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.

[1]  Milos Manic,et al.  General Type-2 Fuzzy C-Means Algorithm for Uncertain Fuzzy Clustering , 2012, IEEE Transactions on Fuzzy Systems.

[2]  Jian Xiao,et al.  Enhanced interval type-2 fuzzy c-means algorithm with improved initial center , 2014, Pattern Recognit. Lett..

[3]  Jerry M. Mendel,et al.  Centroid of a type-2 fuzzy set , 2001, Inf. Sci..

[4]  Witold Pedrycz,et al.  Conditional Fuzzy C-Means , 1996, Pattern Recognit. Lett..

[5]  Jerry M. Mendel,et al.  Uncertain Rule-Based Fuzzy Logic Systems for Wireless Communications , 2001, FUZZ-IEEE.

[6]  Keun-Chang Kwak,et al.  Adaptive Neuro-Fuzzy Networks with the Aid of Fuzzy Granulation , 2005, IEICE Trans. Inf. Syst..

[7]  Leehter Yao,et al.  On A Type-2 Fuzzy Clustering Algorithm , 2012 .

[8]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[9]  Witold Pedrycz,et al.  Linguistic models and linguistic modeling , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[10]  N. N. Karnik,et al.  Introduction to type-2 fuzzy logic systems , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[11]  Athanasios Tsanas,et al.  Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , 2012 .

[12]  Witold Pedrycz,et al.  Conditional fuzzy clustering in the design of radial basis function neural networks , 1998, IEEE Trans. Neural Networks.

[13]  Frank Chung-Hoon Rhee,et al.  Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means , 2007, IEEE Transactions on Fuzzy Systems.