Titlebook: Explainable AI and Other Applications of Fuzzy Techniques; Proceedings of the 2 Julia Rayz,Victor Raskin,Vladik Kreinovich Conference proce

micturition

,Takagi-Sugeno Fuzzy Systems with?Triangular Membership Functions as?Interpretable Neural Networks,equivalence relation between the above mentioned systems under relaxed conditions. As the above proofs are constructive, this method allows us to transform between the considered Neural Networks and Takagi-Sugeno systems. The interpretability of the proposed system is discussed and future research d

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Deadpan

,How Much for a Set: General Case of?Decision Making Under Set-Valued Uncertainty,is financial: we need to select an alternative that will bring us the largest financial gain. The problem is that usually, we only know the gain corresponding to each alternative with some uncertainty: instead of the exact numerical value of this gain, there is a whole set of possible values of this

極小量

A Deep Fuzzy Semi-supervised Approach to Clustering and Fault Diagnosis of Partially Labeled Semicoto create fully labeled training datasets for supervised learning artificial intelligence methods. Semi-supervised learning methods are useful in the realistic scenario where engineers may only be able to annotate limited partial subsets, but existing approaches are limited in scalability for high-d

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legacy

Adrenal-Glands

,A Note on the KM Algorithm for?Computing the Variance of an Interval Type-2 Fuzzy Set,the computation of the relative variance of an interval Type-2 fuzzy set using the KM algorithm needs more computations than using the original KM algorithm in order to obtain their boundaries since the variance combined with the KM algorithm results in a nonlinear behavior. A modified KM algorithm

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