A class of fuzzy random optimization: Expected value models.
type: Item
instance of: scholarly article
title: A class of fuzzy random optimization: Expected value models.
zbMATH Open document ID: 1039.60002
DOI: 10.1016/S0020-0255(03)00079-3
author: Yiankui Liu
author: Baoding Liu
published in: Information Sciences
publication date: 2004-03-15T00:00:00Z
review text: The authors introduce (possibilistic) fuzzy variables and their real-valued expectation which is some kind of defuzzification of the possibility distribution. A fuzzy random variable \(Y\) is a measurable mapping from a probability space to a collection of fuzzy variables. The expected expectation of these fuzzy variables figures as a (scalar) expectation of \(Y\) (which is obviously different from the well-known fuzzy-valued Aumann-expectation). Using this expectation, the authors present three types of optimization models which can be solved by simulation, neural networks or genetic algorithms.
reviewed by: Wolfgang Näther
Mathematics Subject Classification ID: 60A05
Mathematics Subject Classification ID: 90C70
Mathematics Subject Classification ID: 28E10
zbMATH DE Number: 2057903
zbMATH Keywords: fuzzy random variable
zbMATH Keywords: fuzzy random optimization
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 2057903