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It endorses also the advantages of using of a fuzzy inference system to model the uncertainty parameters levels in risk analysis.
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Therefore, the use of a fuzzy controller is interesting to control process performance.
In this framework, the originality of the presented work lies in the use of a fuzzy model and its inversion.
To address this problem, Jusoh et al. [83] introduced the use of a fuzzy lexicon and fuzzy sets in deciding the degree of positive and negative.
In addition, our use of a fuzzy ranking method to eliminate redundant fuzzy connection weights in our FNN architecture results in improved performance over other FNN models.
The key idea of the proposed approach is the use of a fuzzy controller with an adaptive gain as a maximum power point tracker.
This study examines the causal impacts of a school management program on educational outcomes in São Paulo/Brazil, estimated with the use of a fuzzy regression discontinuity design.
Results show that both systems can be modeled successfully by the combined use of a fuzzy approach and neural network learning.
Whilst the HKM performed satisfactorily in the context of MENUET for BSS, the work presented in [21] and [36] demonstrated that the use of a fuzzy clustering algorithm improves the accuracy of mask estimation.
Some fixed quantization schemes support the use of a fuzzy extractor, provided that the quantization intervals can be chosen such that each secret s is equiprobable, as in [17].
In this paper, we focus on the use of a fuzzy system as an agent to construct surrogate models in the form of granules to be used with genetic algorithms.
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