The development of fuzzy radial basis function neural networks based on the concept of information ambiguity

Authors:
Seok-Beom Roh;Su-Chong Joo;Witold Pedrycz;Sung-Kwun Oh
Affiliations:
Department of Electrical Electronic and Information Engineering, Wonkwang University, 344-2, Shinyong-Dong, Iksan, Chon-Buk 570-749, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, 344-2, Shinyong-Dong, Iksan, Chon-Buk 570-749, South Korea;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2G6 and School of Computing Science, University of Nottingham, Nottingham, UK and Systems Researc ...;Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do 445-743, South Korea
Venue:
Neurocomputing
Year:
2010

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Abstract

There is a remarkably rich landscape of fuzzy clustering and ensuing design procedures of information granules. In a nutshell, fuzzy clustering (and clustering, in general) leads to direction-free constructs meaning that there is no clear distinction between input and output variables. In the framework of fuzzy modeling, information granules are used in the development of input-output mapping and from this perspective it becomes beneficial to consider the aspect of directionality in the construction of information granules (fuzzy sets) in the input space. Conditional fuzzy C-means clustering comes as one of the algorithmically viable alternatives using which we construct fuzzy sets over the input space in presence of supervision coming in the form of structure of data distributed over the output space. In this paper, presented is a new clustering method in which we use the ambiguity index to express the boundaries of the clusters. The design is illustrated with the aid of several numeric examples that provide a detailed insight into the performance of the fuzzy models formed in this manner and also highlight several crucial design issues.