Inferring a Possibility Distribution from Very Few Measurements

2009 ◽  
pp. 92-99 ◽  
Author(s):  
Gilles Mauris
Keyword(s):  
Possibility Distribution

On possibility distribution

1995 ◽  
Vol 69 (2) ◽  
pp. 171-179 ◽  
Author(s):  
Yongqiang Cao ◽  
Xiaofei Qu
Keyword(s):  
Possibility Distribution

Quasiconvex possibility distribution with application in decision-making

2018 ◽  
Vol 4 (2) ◽  
pp. 265-274 ◽  
Author(s):  
Lesheng Jin ◽  
Jian Wang ◽  
Gang Qian
Keyword(s):  
Decision Making ◽  
Possibility Distribution

Possibilistic Uncertainty Quantification in 1-D Consolidation Problems

2021 ◽  
Author(s):  
Djamalddine Boumezerane
Keyword(s):  
Uncertainty Quantification ◽  
Probability Distributions ◽  
Uncertainty Propagation ◽  
Possibility Distribution ◽  
Coefficient Of Consolidation ◽  
Consolidation Process ◽  
Possibility Distributions ◽  
Coverage Function ◽  
The One ◽  
Measure Of Uncertainty

Abstract In this study, we use possibility distribution as a basis for parameter uncertainty quantification in one-dimensional consolidation problems. A Possibility distribution is the one-point coverage function of a random set and viewed as containing both partial ignorance and uncertainty. Vagueness and scarcity of information needed for characterizing the coefficient of consolidation in clay can be handled using possibility distributions. Possibility distributions can be constructed from existing data, or based on transformation of probability distributions. An attempt is made to set a systematic approach for estimating uncertainty propagation during the consolidation process. The measure of uncertainty is based on Klir's definition (1995). We make comparisons with results obtained from other approaches (probabilistic…) and discuss the importance of using possibility distributions in this type of problems.

FRACTAL IMAGE COMPRESSION ALGORITHMS BASED ON POSSIBILITY THEORY

Fractals ◽  
2007 ◽  
Vol 15 (02) ◽  
pp. 183-195 ◽  
Author(s):  
RUI YANG ◽  
XIAOYUAN YANG ◽  
B. LI
Keyword(s):  
Image Compression ◽  
Possibility Theory ◽  
Necessary Condition ◽  
Search Process ◽  
Average Intensity ◽  
Possibility Distribution ◽  
Image Block ◽  
Fractal Image Compression ◽  
Compression Algorithms ◽  
Fractal Image

Two fractal image compression algorithms based on possibility theory are originally presented in this paper. Fuzzy sets are used to represent the edge character of each image block, and two kinds of membership function are designed. A fuzzy integrated judgement model is also proposed. The model generates an accurate value for each edge block, which would be a label during the search process. The edge possibility distribution function and the edge necessity level are designed to control the quantity of the blocks to be searched. Meanwhile the pre-restriction is proposed, the average intensity value at different locations is used to be a necessary condition before the MSE computations. It is shown by our experiments that the encoding times of our two algorithms, compared to that of Jacquin's approach, are reduced to 60%–70% and 10%–20%, respectively.

Possibilistic Linear Programming Problems involving Normal Random Variables

2016 ◽  
Vol 5 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Suresh Kumar Barik ◽  
M. P. Biswal
Keyword(s):  
Linear Programming ◽  
Programming Problem ◽  
Random Variables ◽  
Fuzzy Programming ◽  
Solution Procedure ◽  
Mathematical Programming Problem ◽  
Possibility Distribution ◽  
Linear Programming Problems ◽  
The Right ◽  
Possibilistic Linear Programming

A new solution procedure of possibilistic linear programming problem is developed involving the right hand side parameters of the constraints as normal random variables with known means and variances and the objective function coefficients are considered as triangular possibility distribution. In order to solve the proposed problem, convert the problem into a crisp equivalent deterministic multi-objective mathematical programming problem and then solved by using fuzzy programming method. A numerical example is presented to illustrate the solution procedure and developed methodology.

MVB-Based Dynamic Supervision of Docks in Traffic Memory

2011 ◽  
Vol 403-408 ◽  
pp. 2728-2731
Author(s):  
Jiang Ming Deng ◽  
Te Fang Chen ◽  
Shu Cheng
Keyword(s):  
Distribution Function ◽  
Normal Distribution ◽  
Disturbance Rejection ◽  
Information Transfer ◽  
Working Condition ◽  
High Reliability ◽  
Possibility Distribution ◽  
Time Intervals ◽  
Vital Importance

A high reliability of Traffic Memory(TM) working condition is of vital importance to information transfer in MVB. In order to avoid bus traffic overflows, the minimum possible time intervals or loading ratios of TM for a given number of ports were calculated. The longer supervision period it had, the more number of docks could be supervised. The number of docks supervised, during supervision intervals, was submitted to Normal distribution ( Ν(μ,σ2)). From the possibility distribution function it could σfind maximum possibility of the number of docks in working. From the disturbance rejection test of the fixed factor σ , a reasonable setting of sink-time supervision interval could be made to guarantee a high reliability for TM working condition.

THE INDEPENDENCE OF FUZZY VARIABLES WITH APPLICATIONS TO FUZZY RANDOM OPTIMIZATION

2007 ◽  
Vol 15 (supp02) ◽  
pp. 1-20 ◽  
Author(s):  
YIAN-KUI LIU ◽  
JINWU GAO
Keyword(s):  
Distribution Function ◽  
Sufficient Conditions ◽  
Possibility Distribution ◽  
Fuzzy Variables ◽  
Fundamental Properties ◽  
Random Optimization ◽  
Fuzzy Random Programming ◽  
Necessary And Sufficient ◽  
The Relationship ◽  
Fuzzy Random

This paper presents the independence of fuzzy variables as well as its applications in fuzzy random optimization. First, the independence of fuzzy variables is defined based on the concept of marginal possibility distribution function, and a discussion about the relationship between the independent fuzzy variables and the noninteractive (unrelated) fuzzy variables is included. Second, we discuss some properties of the independent fuzzy variables, and establish the necessary and sufficient conditions for the independent fuzzy variables. Third, we propose the independence of fuzzy events, and deal with its fundamental properties. Finally, we apply the properties of the independent fuzzy variables to a class of fuzzy random programming problems to study their convexity.

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