Results 11 to 20 of about 867,994 (193)
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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Real-Time joint Landmark Recognition and Classifier Generation by an Evolving Fuzzy System. [PDF]
, 2006 A new approach to real-time joint classification and classifier design is proposed in this paper. It is based on the recently developed evolving fuzzy system (EFS) method and is applied to mobile robotics.
Angelov, Plamen +3 more
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On-line identification of MIMO evolving Takagi-Sugeno fuzzy models [PDF]
, 2004 Evolving Takagi-Sugeno (eTS) fuzzy models and the method for their on-line identification has been recently introduced as an effective tool for design of flexible system models with minimum a priori information.
Angelov, Plamen, Xydeas, C, Filev, D
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A Two-Stage Evolutionary Fuzzy Clustering Framework for Noisy Image Segmentation
IEEE Access, 2020 This article presents a two-stage evolutionary fuzzy clustering framework for noisy image segmentation. It is a bi-stage system comprising a multi-objective optimization stage and a fuzzy clustering segmentation stage. In the multi-objective optimization
Licheng Jiao +4 more
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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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A Framework of Mutual Information Kullback-Leibler Divergence based for Clustering Categorical Data
JOIV: International Journal on Informatics Visualization, 2021 Clustering is a process of grouping a set of objects into multiple clusters, so that the collection of similar objects will be grouped into the same cluster and dissimilar objects will be grouped into other clusters.
Iwan Tri Riyadi Yanto +3 more
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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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RESAMPLING FOR FUZZY CLUSTERING [PDF]
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2007 Resampling methods are among the best approaches to determine the number of clusters in prototype-based clustering. The core idea is that with the right choice for the number of clusters basically the same cluster structures should be obtained from subsamples of the given data set, while a wrong choice should produce considerably varying cluster ...
openaire +3 more sources