Results 231 to 240 of about 220,242 (290)

Selecting self-and-follower goal-aware leadership styles across sectors: a decision support approach. [PDF]

Front Psychol
Czukor G, Yüksel S, Eti S, Dinçer H.
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Entropy-Augmented Forecasting and Portfolio Construction at the Industry-Group Level: A Causal Machine-Learning Approach Using Gradient-Boosted Decision Trees. [PDF]

Entropy (Basel)
Cohen G, Aiche A, Eichel R.
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An enhanced fuzzy credibility bidirectional projection based multi-attribute group decision making and its application in sports equipment supplier selection. [PDF]

Sci Rep
Liu L.
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Design of global climate control based on fuzzy systems with concept of carbon emissions. [PDF]

PLoS One
Naseer S, Chang WJ, Aslam MS, Hashim Bukhari M, Bilal H, Nawaz MJ.   +5 more
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Corrigendum to "Selecting optimal celestial object for space observation in the realm of complex spherical fuzzy systems" [Heliyon Volume 10, Issue 13, July 2024, Article e32897]. [PDF]

Heliyon
Razzaque A, Ansari MN, Alghazzawi D, Khalifa HAE, Alburaikan A, Razaq A.   +5 more
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Applications of Fuzzy Set Theory, Fuzzy Measure Theory and Fuzzy Differential Calculus

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Fuzzy similarity measures and measurement theory

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019
We consider objects associated with a fuzzy set-based representation. By using a classic method of measurement introduced by Tversky, we establish necessary and sufficient conditions for the existence of a particular class of fuzzy similarity measures, agreeing with an ordering relation among pairs of objects which express the idea that two objects are
Coletti, Giulianella, Bouchon-Meunier, Bernadette   +1 more
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Fuzzy measurement theory

Measurement, 2008
Abstract Exact measurement is a mapping f0 from the structure of physical objects into the structure of real numbers R representing the results of measurement. In the framework of the fuzzy theory of measurement an inexact measurement is represented by a mapping f from a physical objects into a structure of fuzzy intervals.
Michał K. Urbański, Janusz Wa¸sowski
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Fuzzy measure based on decomposition theory

Fuzzy Sets and Systems, 2000
The authors define a fuzzy measure \({\mathfrak M}^*\) for fuzzy numbers \(D\) by the formula \({\mathfrak M}^*(D)= \int^1_0(D_r(\alpha)- D_1(\alpha)) d\alpha\), where \(D_r(\alpha)\) and \(D_1(\alpha)\) are, respectively, the upper and lower bound of the \(\alpha\)-cut \(\{x\in \mathbb{R}:D(x)\geq \alpha\}= [D_1(\alpha), D_r(\alpha)]\).
Jing-Shing Yao, San-Chyi Chang
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