The Experts below are selected from a list of 13824 Experts worldwide ranked by ideXlab platform
Byungin Choi - One of the best experts on this subject based on the ideXlab platform.
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interval type 2 Fuzzy Membership Function generation methods for pattern recognition
Information Sciences, 2009Co-Authors: Byungin Choi, Frank Chunghoon RheeAbstract:Type-2 Fuzzy sets (T2 FSs) have been shown to manage uncertainty more effectively than T1 Fuzzy sets (T1 FSs) in several areas of engineering [4,6-12,15-18,21-27,30]. However, computing with T2 FSs can require undesirably large amount of computations since it involves numerous embedded T2 FSs. To reduce the complexity, interval type-2 Fuzzy sets (IT2 FSs) can be used, since the secondary Memberships are all equal to one [21]. In this paper, three novel interval type-2 Fuzzy Membership Function (IT2 FMF) generation methods are proposed. The methods are based on heuristics, histograms, and interval type-2 Fuzzy C-means. The performance of the methods is evaluated by applying them to back-propagation neural networks (BPNNs). Experimental results for several data sets are given to show the effectiveness of the proposed Membership assignments.
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interval type 2 Fuzzy Membership Function design and its application to radial basis Function neural networks
IEEE International Conference on Fuzzy Systems, 2007Co-Authors: Frank Chunghoon Rhee, Byungin ChoiAbstract:Type-2 Fuzzy sets has been shown to manage uncertainty more effectively than type-1 Fuzzy sets in several pattern recognition applications. However, computing with type-2 Fuzzy sets can require high computational complexity since it involves numerous embedded type-2 Fuzzy sets. To reduce the complexity, interval type-2 Fuzzy sets can be used. In this paper, an interval type-2 Fuzzy Membership design method and its application to radial basis Function (RBF) neural networks is proposed. Type-1 Fuzzy Memberships which are computed from the centroid of the interval type-2 Fuzzy Memberships are incorporated into the RBF neural network The proposed Membership assignment is shown to improve the classification performance of the RBF neural network since the uncertainty of pattern data are desirably controlled by interval type-2 Fuzzy Memberships. Experimental results for several data sets are given.
Frank Chunghoon Rhee - One of the best experts on this subject based on the ideXlab platform.
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interval type 2 Fuzzy Membership Function generation methods for pattern recognition
Information Sciences, 2009Co-Authors: Byungin Choi, Frank Chunghoon RheeAbstract:Type-2 Fuzzy sets (T2 FSs) have been shown to manage uncertainty more effectively than T1 Fuzzy sets (T1 FSs) in several areas of engineering [4,6-12,15-18,21-27,30]. However, computing with T2 FSs can require undesirably large amount of computations since it involves numerous embedded T2 FSs. To reduce the complexity, interval type-2 Fuzzy sets (IT2 FSs) can be used, since the secondary Memberships are all equal to one [21]. In this paper, three novel interval type-2 Fuzzy Membership Function (IT2 FMF) generation methods are proposed. The methods are based on heuristics, histograms, and interval type-2 Fuzzy C-means. The performance of the methods is evaluated by applying them to back-propagation neural networks (BPNNs). Experimental results for several data sets are given to show the effectiveness of the proposed Membership assignments.
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interval type 2 Fuzzy Membership Function design and its application to radial basis Function neural networks
IEEE International Conference on Fuzzy Systems, 2007Co-Authors: Frank Chunghoon Rhee, Byungin ChoiAbstract:Type-2 Fuzzy sets has been shown to manage uncertainty more effectively than type-1 Fuzzy sets in several pattern recognition applications. However, computing with type-2 Fuzzy sets can require high computational complexity since it involves numerous embedded type-2 Fuzzy sets. To reduce the complexity, interval type-2 Fuzzy sets can be used. In this paper, an interval type-2 Fuzzy Membership design method and its application to radial basis Function (RBF) neural networks is proposed. Type-1 Fuzzy Memberships which are computed from the centroid of the interval type-2 Fuzzy Memberships are incorporated into the RBF neural network The proposed Membership assignment is shown to improve the classification performance of the RBF neural network since the uncertainty of pattern data are desirably controlled by interval type-2 Fuzzy Memberships. Experimental results for several data sets are given.
Jonathan M Garibaldi - One of the best experts on this subject based on the ideXlab platform.
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effect of type 2 Fuzzy Membership Function shape on modelling variation in human decision making
IEEE International Conference on Fuzzy Systems, 2004Co-Authors: T Ozen, Jonathan M GaribaldiAbstract:This paper explains how the shape of type-2 Fuzzy Membership Functions can be used to model the variation in human decision making. An interval type-2 Fuzzy logic system (FLS) is developed for umbilical acid-base assessment. The influence of the shape of the Membership Functions on the variation in decision making of the Fuzzy logic system is studied using the interval outputs. Three different methods are used to create interval type-2 Membership Functions. The centre points of the primary Membership Functions are shifted, the widths are shifted, and a uniform band is introduced around the original type-1 Membership Functions. It is shown that there is a direct relationship between the variation in decision making and the uncertainty introduced to the Membership Functions.
T Ozen - One of the best experts on this subject based on the ideXlab platform.
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effect of type 2 Fuzzy Membership Function shape on modelling variation in human decision making
IEEE International Conference on Fuzzy Systems, 2004Co-Authors: T Ozen, Jonathan M GaribaldiAbstract:This paper explains how the shape of type-2 Fuzzy Membership Functions can be used to model the variation in human decision making. An interval type-2 Fuzzy logic system (FLS) is developed for umbilical acid-base assessment. The influence of the shape of the Membership Functions on the variation in decision making of the Fuzzy logic system is studied using the interval outputs. Three different methods are used to create interval type-2 Membership Functions. The centre points of the primary Membership Functions are shifted, the widths are shifted, and a uniform band is introduced around the original type-1 Membership Functions. It is shown that there is a direct relationship between the variation in decision making and the uncertainty introduced to the Membership Functions.
Mohamed S Kamel - One of the best experts on this subject based on the ideXlab platform.
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generation of Fuzzy Membership Function using information theory measures and genetic algorithm
Lecture Notes in Computer Science, 2003Co-Authors: Masoud Makrehchi, Otman A Basir, Mohamed S KamelAbstract:One of the most challenging issues in Fuzzy systems design is generating suitable Membership Functions for Fuzzy variables. This paper proposes a paradigm of applying an information theoretic model to generate Fuzzy Membership Functions. After modeling Fuzzy Membership Function by Fuzzy partitions, a genetic algorithm based optimization technique is presented to find sub optimal Fuzzy partitions. To generate Fuzzy Membership Function based on Fuzzy partitions, a heuristic criterion is also defined. Extensive numerical results and evaluation procedure are provided to demonstrate the effectiveness of the proposed paradigm.
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IFSA - Generation of Fuzzy Membership Function using information theory measures and genetic algorithm
Lecture Notes in Computer Science, 2003Co-Authors: Masoud Makrehchi, Otman A Basir, Mohamed S KamelAbstract:One of the most challenging issues in Fuzzy systems design is generating suitable Membership Functions for Fuzzy variables. This paper proposes a paradigm of applying an information theoretic model to generate Fuzzy Membership Functions. After modeling Fuzzy Membership Function by Fuzzy partitions, a genetic algorithm based optimization technique is presented to find sub optimal Fuzzy partitions. To generate Fuzzy Membership Function based on Fuzzy partitions, a heuristic criterion is also defined. Extensive numerical results and evaluation procedure are provided to demonstrate the effectiveness of the proposed paradigm.
