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Fuzzy T-equivalence relations constitute the fundamentals of most fuzzy rough set models.
In contrast to most fuzzy models encountered in the literature, the results produced by granular models are information granules rather than plain numeric entities.
Most fuzzy yarns will tangle easily and rip to shreds if you frog them, plus they're usually harder to care for.
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The most prominent fuzzy clustering algorithm is the fuzzy c-means clustering [43].
Unlike most evolutionary fuzzy systems, where the structure of the fuzzy system is assigned in advance, an on-line fuzzy clustering approach is proposed for system structure learning.
The chapter also reviews the most important fuzzy clustering techniques and shows their relationship to non-fuzzy approaches.
Most proposed fuzzy neural networks in the literature could be classified into two categories.
An algorithm was developed to extract the most dominant fuzzy rules.
The novelty in the presented approach, as compared to the most recent fuzzy ones, stems from its generality.
The bibliographical analysis, supplemented with statistics of relevant research publications, has allowed to allocate the most important fuzzy application cases for each domain.
Most analytic fuzzy approaches are derived from Bezdek's FCM algorithm applied to the grey level images to automatically determine the membership degree of each pixel.
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