Visualization of two-dimensional interval type-2 fuzzy membership functions using general type-2 fuzzy membership functions

In this paper, we propose a novel method for visualizing two-dimensional interval type-2 fuzzy membership functions (2-D IT2 FMFs) using one-dimensional general type-2 fuzzy membership functions (1-D GT2 FMFs), and also describe the procedure for extending our method to fuzzy sets representing higher dimensional data. Then we present a type reduction method for mapping 2-D IT2 fuzzy sets into 2-D type-1 fuzzy sets that uses alpha-plane representation of general fuzzy sets. We discuss the problem of “multiple membership values for the same element,” which violates set properties, in an IT2 Fuzzy C-means (FCM) algorithm for clustering and propose a solution that uses transformations in the visualization method. These techniques can be applied to applications involving fuzzy sets that represent multidimensional data for proper visualization and type reduction, such as image segmentation, classification and prediction, to name a few.

[1]  Jerry M. Mendel,et al.  Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters , 2000, IEEE Trans. Fuzzy Syst..

[2]  Robert A. Meyers Computational complexity : theory, techniques, and applications , 2012 .

[3]  Jerry M. Mendel,et al.  Enhanced Centroid-Flow Algorithm for Computing the Centroid of General Type-2 Fuzzy Sets , 2012, IEEE Transactions on Fuzzy Systems.

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

[5]  Jerry M. Mendel,et al.  MPEG VBR video traffic modeling and classification using fuzzy technique , 2001, IEEE Trans. Fuzzy Syst..

[6]  Feilong Liu,et al.  An efficient centroid type-reduction strategy for general type-2 fuzzy logic system , 2008, Inf. Sci..

[7]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[8]  Jerry M. Mendel,et al.  Enhanced Karnik--Mendel Algorithms , 2009, IEEE Transactions on Fuzzy Systems.

[9]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[10]  M. M. Morcos,et al.  Application of AI tools in fault diagnosis of electrical machines and drives-an overview , 2003 .

[11]  Oscar Castillo,et al.  A new Interval Type-2 Fuzzy Possibilistic C-Means clustering algorithm , 2015, 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC).

[12]  Jonathan M. Garibaldi,et al.  Type-2 fuzzy medical diagnosis , 2002 .

[13]  Frank Chung-Hoon Rhee,et al.  Interval type-2 approach to kernel possibilistic C-means clustering , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[14]  Jerry M. Mendel,et al.  Introduction to Type-2 Fuzzy Logic Control: Theory and Applications , 2014 .

[15]  J. Mendel,et al.  α-Plane Representation for Type-2 Fuzzy Sets: Theory and Applications , 2009 .

[16]  Jerry M. Mendel,et al.  Super-Exponential Convergence of the Karnik-Mendel Algorithms Used for Type-reduction in Interval Type-2 Fuzzy Logic Systems , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[17]  Jerry M. Mendel,et al.  $\alpha$-Plane Representation for Type-2 Fuzzy Sets: Theory and Applications , 2009, IEEE Transactions on Fuzzy Systems.

[18]  Byung-In Choi,et al.  Interval type-2 fuzzy membership function generation methods for pattern recognition , 2009, Inf. Sci..

[19]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[20]  F. Chung-Hoon Rhee Uncertain Fuzzy Clustering: Insights and Recommendations , 2007 .

[21]  K. Wu Fuzzy interval control of mobile robots , 1996 .

[22]  Hani Hagras,et al.  Type-2 Fuzzy Logic Controllers: A Way Forward for Fuzzy Systems in Real World Environments , 2008, WCCI.

[23]  László T. Kóczy,et al.  Fuzzy rule interpolation for multidimensional input spaces with applications: a case study , 2005, IEEE Transactions on Fuzzy Systems.