Results 11 to 20 of about 926,739 (292)
On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data
, 2017 Since the notion of hesitant fuzzy set was introduced, some clustering algorithms have been proposed to cluster hesitant fuzzy data. Beside of hesitation in data, there is some hesitation in the clustering (classification) of a crisp data set.
Esfandiar Eslami +5 more
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Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2000 The well-known generalization of hard C-means (HCM) clustering is fuzzy C-means (FCM) clustering where a weight exponent on each fuzzy membership is introduced as the degree of fuzziness.
Wagner, Michael +3 more
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On-line evolution of Takagi-Sugeno fuzzy models [PDF]
, 2004 Evolving Takagi-Sugeno (eTS) fuzzy models and the method for their on-line identification has been recently introduced for both MISO and MIMO case. In this paper, the mechanism for rule-base evolution, one of the central points of the algorithm together ...
Angelov, Plamen +7 more
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Fuzzy clustering of fuzzy systems [PDF]
2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), 2005 A fuzzy system entirely characterizes one region of the input-output product space S = U /spl times/ V through a relation expressed by a set of fuzzy rules. Effectively, the fuzzy system establishes a fuzzy map, which assigns for each input fuzzy set in U an output fuzzy set in V.
Salgado, Paulo, Garrido, Paulo
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Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters [PDF]
Computational Intelligence and Neuroscience, 2013 We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived.
Tina Geweniger +4 more
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Clustering as a tool for self-generation of intelligent systems : a survey. [PDF]
, 2010 Fuzzy Rule Based (FRB) and Neuro-fuzzy systems are commonly used as a basis for intelligent systems due to their transparent and simple human interpretable structure.
Angelov, Plamen, Dutta Baruah, Rashmi
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Fuzzy Ants and Clustering [PDF]
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2007 A swarm-intelligence-inspired approach to clustering data is described. The algorithm consists of two stages. In the first stage of the algorithm, ants move the cluster centers in feature space. The cluster centers found by the ants are evaluated using a reformulated fuzzy C-means (FCM) criterion. In the second stage, the best cluster centers found are
Parag M. Kanade, Lawrence O. Hall
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General fuzzy min-max neural network for clustering and classification [PDF]
, 2000 This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993).
Gabrys, Bogdan, Bargiela, Andrzej
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Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering [PDF]
, 2009 Let Q be a given n×n square symmetric matrix of nonnegative elements between 0 and 1, similarities. Fuzzy clustering results in fuzzy assignment of individuals to K clusters.
Braak, C.J.F., ter +3 more
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International Journal of Computational Intelligence Systems, 2020 In the big data background, the uncertainty of data is increasingly apparent. Multi-polar fuzzy feature of data has been more popularly used by the research community for the purpose of the classification of weighing cheating in dynamic truck scale ...
Zhenyu Lu, Xianyun Huang
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