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Determining whether this activity is large ("sharp" fragment) or small ("quiet" fragment) is performed by using fuzzy comparisons (Gvishiani et al., 2008a, b) between a large number of fragments of varying lengths Λ = {Δ1,…, Δ m }.
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Fuzzy extensions of AHP are usually in the form of fuzzifying the pairwise comparisons by using fuzzy numbers.
This is achieved by using fuzzy sets to model sonar data and by using fuzzy triangulation to identify robot's position and orientation.
By using fuzzy modeling method, a design scheme of an adaptive fuzzy controller is proposed.
When fuzzy systems are designed by using fuzzy clustering, each cluster corresponds to a fuzzy rule.
Enhancing contrast by using fuzzy clustering allows to define the contours of the lesion correctly.
Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix.
With the contrast enhancement by using fuzzy clustering an output that adequately defines the contours of the lesions is obtained.
Different from black-box methods, fuzzy rule-based models can not only provide good classification results, but also easily be explained and interpreted in human understandable terms by using fuzzy rules.
Our architecture, shown in Figure2, addresses this capability by using fuzzy-Adaptive Resonance Theory (Fuzzy ART) artificial neural network modules.
We hypothesized that the fuzzy measure of prediction ( F Pred (A,B)) defined by us using fuzzy subsethood and measured in fuzzy cardinality can be used to precisely compare an individual clinical patient to the average patient of a large double blind randomized controlled clinical trial.
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