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The advantage of the proposed control scheme is that it can better handle the vagueness or uncertainties inherent in linguistic words using fuzzy set membership functions with adaptation capability by linear analytical results instead of estimating non-linear system functions as the system parameters are unknown.
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Figure 3a displays evolution of fitness over 1,000 generations using fuzzy sets with six possible membership functions as suggested by Shi et al. Repetition yielded comparable fitness values, indicating that searches ended in the same minimum.
On the other hand, the overlapping clustering uses fuzzy sets to cluster data, so that each point may belong to two or more clusters with different degrees of membership.
This approach uses fuzzy-set scores to present the degree of membership in explanatory conditions and results.
Also, fsQCA provides a method for calibration and quantification of inherently "fuzzy" qualitative data (fuzzy set membership assignment) that enables systematic analysis using both qualitative comparative analysis and standard statistical methods.
With a fuzzy set, membership is a bit uncertain.
The fuzzy argument compares the data values against a fuzzy set membership function that returns a level of trueness based on the degree of membership in the fuzzy set.
It is at this logical node that the overall fuzzy set membership of a dependency network is calculated.
In NetWeaver™, fuzzy set membership is measured on a scale of −1 (no membership in the fuzzy set "true," which is equivalent to 100%% "false"), to 0 ("undetermined or neutral" in the case of insufficient data) to 1 (complete membership in the fuzzy set "true" which is equivalent to 100%% "true").
Every FES has a fuzzy inference system that reasons using fuzzy logic membership functions, which refers to the degree to which the value of a particular attribute belongs to a set.
Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix.
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