Reliability Assessment Using Fuzzy Evidence Theory

2012 ◽  
Vol 220-223 ◽  
pp. 2165-2168
Author(s):  
Xing Yuan Zhang ◽  
Hai Rong Xu ◽  
Da Min Cao ◽  
Sheng Bin Hu
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Theory ◽  
Evidence Theory ◽  
Reliability Assessment ◽  
Assessment Process ◽  
New Approach ◽  
Inclusion Degree ◽  
Finite Set ◽  
Aircraft System ◽  
Theory Of Evidence

Aiming at many uncertain factors in the reliability assessment of aircraft, a new approach to reliability assessment combining D-S theory of evidence and fuzzy theory was discussed. The concepts of inclusion degree and intersection degree of fuzzy sets were redefined, and fuzzy evidence theory was extended to the circumstance when the framework of discrimination is a continuous and in finite set. Finally, the practical assessment process was given in an illustrative example of aircraft system. The results show that the method is effective and reliable.

scholarly journals Method of Information Security Risk Assessment Based on Improved Fuzzy Theory of Evidence

2018 ◽  
Vol 14 (03) ◽  
pp. 188 ◽  
Author(s):  
Xuepeng Huang ◽  
Wei Xu
Keyword(s):  
Risk Assessment ◽  
Information Systems ◽  
Information Security ◽  
Fuzzy Sets ◽  
Fuzzy Theory ◽  
Security Risk ◽  
Basic Probability ◽  
Information Security Risk ◽  
Security Risk Assessment ◽  
Theory Of Evidence

<p>A method based on improved fuzzy theory of evidence was presented to solve the problem that there exist all kinds of uncertainty in the process of information security risk assessment. The hierarchy model for the information systems risk assessment was established firstly, and then fuzzy sets were introduced into theory of evidence. The basic probability assignments were constructed using the membership function of fuzzy sets, and the basic probability assignments were determined. Moreover, weight coefficients were calculated using entropy weight and empirical factor, which combined the objective weights with the subjective ones, and improved the validity and reliability. An illustration example indicates that the method is feasible and effective, and provides reasonable data for constituting the risk control strategy of the information systems security.</p>

A new approach to reliability assessment of dental caries examinations

2012 ◽  
Vol 41 (4) ◽  
pp. 309-316 ◽  
Author(s):  
Yacoub Altarakemah ◽  
Mona Al-Sane ◽  
Sungwoo Lim ◽  
Albert Kingman ◽  
Amid I. Ismail
Keyword(s):  
Dental Caries ◽  
Reliability Assessment ◽  
New Approach

Dempster-Shafer theory of evidence: A new approach to spatially model wildfire risk potential in central Chile

2018 ◽  
Vol 613-614 ◽  
pp. 1024-1030 ◽  
Author(s):  
Cristián González ◽  
Miguel Castillo ◽  
Pablo García-Chevesich ◽  
Juan Barrios
Keyword(s):  
Central Chile ◽  
New Approach ◽  
Wildfire Risk ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Risk Potential

scholarly journals Analysis of “mixing” combination rules and Smet’s combination rule

2019 ◽  
pp. 4-8
Author(s):  
Sofiia Alpert
Keyword(s):  
Satellite Images ◽  
Probability Distributions ◽  
Evidence Theory ◽  
Combination Rule ◽  
Multiple Sources ◽  
Combination Rules ◽  
Spectral Bands ◽  
Basic Probability ◽  
Shafer Theory ◽  
Theory Of Evidence

The process of solution of different practical and ecological problems, using hyperspectral satellite images usually includes a procedure of classification. Classification is one of the most difficult and important procedures. Some image classification methods were considered and analyzed in this work. These methods are based on the theory of evidence. Evidence theory can simulate uncertainty and process imprecise and incomplete information. It were considered such combination rules in this paper: “mixing” combination rule (or averaging), convolutive x-averaging (or c-averaging) and Smet’s combination rule. It was shown, that these methods can process the data from multiple sources or spectral bands, that provide different assessments for the same hypotheses. It was noted, that the purpose of aggregation of information is to simplify data, whether the data is coming from multiple sources or different spectral bands. It was shown, that Smet’s rule is unnormalized version of Dempster rule, that applied in Smet’s Transferable Belief Model. It also processes imprecise and incomplete data. Smet’s combination rule entails a slightly different formulation of Dempster-Shafer theory. Mixing (or averaging) rule was considered in this paper too. It is the averaging operation that is used for probability distributions. This rule uses basic probability assignments from different sources (spectral bands) and weighs assigned according to the reliability of the sources. Convolutive x-averaging (or c-averaging) rule was considered in this paper too. This combination rule is a generalization of the average for scalar numbers. This rule is commutative and not associative. It also was noted, that convolutive x-averaging (c-averaging) rule can include any number of basic probability assignments. It were also considered examples, where these proposed combination rules were used. Mixing, convolutive x-averaging (c-averaging) rule and Smet’s combination rule can be applied for analysis of hyperspectral satellite images, in remote searching for minerals and oil, solving different environmental and thematic problems.

scholarly journals A Fuzzy Control Chart Approach for Attributes and Variables

2018 ◽  
Vol 8 (5) ◽  
pp. 3360-3365 ◽  
Author(s):  
N. Pekin Alakoc ◽  
A. Apaydin
Keyword(s):  
Quality Control ◽  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Fuzzy Theory ◽  
New Approach ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Quality Control Chart

The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach has better performance and can detect the process shifts efficiently.

scholarly journals Evidence Theory in Picture Fuzzy Set Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Harish Garg ◽  
R. Sujatha ◽  
D. Nagarajan ◽  
J. Kavikumar ◽  
Jeonghwan Gwak
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Evidence Theory ◽  
Belief Function ◽  
Uncertain Information ◽  
Interval Probability ◽  
Dempster Shafer Theory ◽  
Effective Manner ◽  
Picture Fuzzy Set ◽  
Shafer Theory

Picture fuzzy set is the most widely used tool to handle the uncertainty with the account of three membership degrees, namely, positive, negative, and neutral such that their sum is bound up to 1. It is the generalization of the existing intuitionistic fuzzy and fuzzy sets. This paper studies the interval probability problems of the picture fuzzy sets and their belief structure. The belief function is a vital tool to represent the uncertain information in a more effective manner. On the other hand, the Dempster–Shafer theory (DST) is used to combine the independent sources of evidence with the low conflict. Keeping the advantages of these, in the present paper, we present the concept of the evidence theory for the picture fuzzy set environment using DST. Under this, we define the concept of interval probability distribution and discuss its properties. Finally, an illustrative example related to the decision-making process is employed to illustrate the application of the presented work.

A new approach for computing with fuzzy sets using interval analysis

2007 ◽  
Author(s):  
Arunas Mazeika ◽  
Luc Jaulin ◽  
Christophe Osswald
Keyword(s):  
Fuzzy Sets ◽  
Interval Analysis ◽  
New Approach
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