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Fuzzy Measure Theory
Zhenyuan Wang,George J. Klir +1 more
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Introduction.Abstract:
Introduction. Required Background in Set Theory. Fuzzy Measures. Extensions. Structural Characteristics for Set Functions. Measurable Functions on Fuzzy Measure Spaces. Fuzzy Integrals. PanIntegrals. Applications. Index.read more
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Journal ArticleDOI
Conceptual foundations of quantum mechanics: the role of evidence theory, quantum sets, and modal logic
TL;DR: In this paper, a modal logic interpretation of quantum mechanics and quantum set theory is proposed, based on the concept of possible worlds, which is grounded on previous work of a number of researchers (Resconi, Klir, Harmanec) who showed how to represent evidence theory and other uncertainty theories in terms of Modal Logic.
Journal ArticleDOI
Transformation theorems for extended lower and upper Sugeno integrals
TL;DR: The main properties of extended extremal fuzzy measures are considered and several versions of their representation are given and several transformation theorems for extended lower and upper Sugeno integrals are proved.
Proceedings ArticleDOI
Using a new type of nonlinear integral for multi-regression: an application of evolutionary algorithms in data mining
TL;DR: A nonlinear multi-regression model based on the Wang integral to describe a multi-input single-output system that can be used to make prediction when the values of input attributes x/sub 1/, X/sub 2/, ..., X/ sub n/, are known.
Journal ArticleDOI
A novel fuzzy classifier with Choquet integral-based grey relational analysis for pattern classification problems
TL;DR: The proposed fuzzy classifier is a one-class-in-one-network structure consisting of multiple novel single-layer perceptrons that employs the grey relational analysis to compute the grades of relationship for individual attributes.
Journal Article
Theory and application of the composed fuzzy measure of L-measure and delta-measures
TL;DR: Experimental result shows that the Choquet integral regression models with respect to extensional L-measure based on γ-support outperforms others forecasting models.