On the expected value of ℬ-valued random fuzzy variable

2011 ◽  
Author(s):  
Juan Wen ◽  
Hongyan Ma ◽  
Lixing Yang
Keyword(s):  
Fuzzy Variable ◽  
Expected Value ◽  
Random Fuzzy Variable

Expected Value Operator of Random Fuzzy Variable and Random Fuzzy Expected Value Models

2003 ◽  
Vol 11 (02) ◽  
pp. 195-215 ◽  
Author(s):  
Yian-Kui Liu ◽  
Baoding Liu
Keyword(s):  
Fuzzy Variable ◽  
Expected Value ◽  
Fuzzy Simulation ◽  
Hybrid Intelligent Algorithm ◽  
Random Fuzzy Variable ◽  
Decision Systems ◽  
Expected Value Operator ◽  
Fuzzy Expected Value ◽  
Possibility Space ◽  
Definition Of

Random fuzzy variable is mapping from a possibility space to a collection of random variables. This paper first presents a new definition of the expected value operator of a random fuzzy variable, and proves the linearity of the operator. Then, a random fuzzy simulation approach, which combines fuzzy simulation and random simulation, is designed to estimate the expected value of a random fuzzy variable. Based on the new expected value operator, three types of random fuzzy expected value models are presented to model decision systems where fuzziness and randomness appear simultaneously. In addition, random fuzzy simulation, neural networks and genetic algorithm are integrated to produce a hybrid intelligent algorithm for solving those random fuzzy expected valued models. Finally, three numerical examples are provided to illustrate the feasibility and the effectiveness of the proposed algorithm.

scholarly journals A FUZZY BASED APPROACH FOR EARLY REQUIREMENT PRIORITIZATION

2015 ◽  
Vol 15 (2) ◽  
pp. 6480-6490
Author(s):  
Mohd Muqeem ◽  
Dr. Md. Rizwan Beg
Keyword(s):  
Software Development ◽  
Early Phase ◽  
Fuzzy Variable ◽  
Requirement Engineering ◽  
Expected Value ◽  
Inference System ◽  
Complex Time ◽  
Fuzzy Variables ◽  
Expected Value Operator ◽  
Requirement Prioritization

The importance of the prioritization in commercial software development has been analyzed by many researchers. The gathered requirements are required to be put into an order of some priority. In other words we can say that there is a need to prioritize the requirements. It is evident that most of the approaches and techniques proposed in recent research to prioritize the requirements have not been widely adopted. These approaches are too complex, time consuming, or inconsistent and difficult to implement In this paper we propose a fuzzy based approach for requirement prioritization in which  requirement are prioritized in early phase of requirement engineering as post elicitation step. This category of prioritization is known as early requirement prioritization. The proposed fuzzy based approach considers the nature of requirements by modeling their attributes as fuzzy variables. As such, these variables are integrated into a fuzzy based inference system in which the requirements represented as input attributes and ranked via the expected value operator of a fuzzy variable.

ANALYSIS AND ALGORITHMS OF BIFUZZY SYSTEMS

2004 ◽  
Vol 12 (03) ◽  
pp. 357-376 ◽  
Author(s):  
JIAN ZHOU ◽  
BAODING LIU
Keyword(s):  
Numerical Experiments ◽  
Fuzzy Variable ◽  
Expected Value ◽  
Real Numbers ◽  
Fuzzy Variables ◽  
Chance Distribution ◽  
Expected Value Operator ◽  
Possibility Space ◽  
Bifuzzy Variable

A fuzzy variable is a function from a possibility space to the set of real numbers, while a bifuzzy variable is a function from a possibility space to the set of fuzzy variables. In this paper, a concept of chance distribution is originally presented for bifuzzy variable, and the linearity of expected value operator of bifuzzy variable is proved. Furthermore, bifuzzy simulations are designed and illustrated by some numerical experiments.

The First Moments and Semi-Moments of Fuzzy Variables Based on an Optimism-Pessimism Measure with Application for Portfolio Selection

2020 ◽  
Vol 16 (02) ◽  
pp. 271-290
Author(s):  
Justin Dzuche ◽  
Christian Deffo Tassak ◽  
Jules Sadefo Kamdem ◽  
Louis Aimé Fono
Keyword(s):  
Linear Combination ◽  
Portfolio Selection ◽  
Fuzzy Variable ◽  
Expected Value ◽  
Mathematical Science ◽  
Optimal Portfolios ◽  
Fuzzy Variables ◽  
Convex Linear Combination

Possibility, necessity and credibility measures are used in the literature in order to deal with imprecision. Recently, Yang and Iwamura [L. Yang and K. Iwamura, Applied Mathematical Science 2(46) (2008) 2271–2288] introduced a new measure as convex linear combination of possibility and necessity measures and they determined some of its axioms. In this paper, we introduce characteristics (parameters) of a fuzzy variable based on that measure, namely, expected value, variance, semi-variance, skewness, kurtosis and semi-kurtosis. We determine some properties of these characteristics and we compute them for trapezoidal and triangular fuzzy variables. We display their application for the determination of optimal portfolios when assets returns are described by triangular or trapezoidal fuzzy variables.

Portfolio selection problems with random fuzzy variable returns

2009 ◽  
Vol 160 (18) ◽  
pp. 2579-2596 ◽  
Author(s):  
Takashi Hasuike ◽  
Hideki Katagiri ◽  
Hiroaki Ishii
Keyword(s):  
Portfolio Selection ◽  
Fuzzy Variable ◽  
Random Fuzzy Variable ◽  
Selection Problems
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