FIF: A fuzzy information fusion algorithm based on multi-criteria decision making

2014 ◽  
Vol 58 ◽  
pp. 23-32 ◽  
Author(s):  
Rita A. Ribeiro ◽  
António Falcão ◽  
André Mora ◽  
José M. Fonseca
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Multi Criteria Decision Making ◽  
Fuzzy Information ◽  
Fusion Algorithm

An Approach Based on Definite Integrals to Multi-Criteria Decision Making with Correlative Intuitionistic Fuzzy Information

2016 ◽  
Vol 24 (06) ◽  
pp. 807-829 ◽  
Author(s):  
Shan Yu ◽  
Zeshui Xu
Keyword(s):  
Decision Making ◽  
Fuzzy Measure ◽  
Multi Criteria Decision Making ◽  
Fuzzy Information ◽  
Definite Integrals ◽  
Intuitionistic Fuzzy ◽  
Technology Improvement ◽  
Fusion Methods

Integration plays a very important role in the fusion methods. In this paper, we put forward the subtraction and division definite integrals based on the fuzzy measure and the admissible order for aggregating not only discrete but also continuous correlative intuitionistic fuzzy information. These definite integrals are implemented by constructing the integrands and the integral limits respectively, and based on which an approach to multi-criteria decision making with correlative intuitionistic fuzzy information is developed. Finally, an illustrative example involving technology improvement of Midwest American Manufacturing Corp is employed to verify the practicality and effectiveness of our approach.

Satisfaction Degree Based Interactive Decision Making under Hesitant Fuzzy Environment with Incomplete Weights

2014 ◽  
Vol 22 (04) ◽  
pp. 553-572 ◽  
Author(s):  
Huchang Liao ◽  
Zeshui Xu
Keyword(s):  
Decision Making ◽  
Hesitant Fuzzy Set ◽  
Optimization Models ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Fuzzy Information ◽  
Satisfaction Degree ◽  
Interactive Decision Making ◽  
New Research ◽  
Topsis Technique

Multi-criteria decision making with hesitant fuzzy information is a new research topic since the hesitant fuzzy set was firstly proposed. This paper investigates a multi-criteria decision making problem where the weight information is partially known. We firstly propose the hesitant fuzzy positive ideal solution and the hesitant fuzzy negative ideal solution. Motivated by the TOPSIS (Technique for Order Preference by Similarity to an ideal Solution) method, we definite the satisfaction degree of an alternative, based on which several optimization models are derived to determinate the weights. Subsequently, in order to make a more reasonable decision, we introduce an interactive method based on some optimization models for multi-criteria decision making problems with hesitant fuzzy information. Finally, a practical example on evaluating the service quality of airlines is provided to illustrate our models and method.

scholarly journals Complex Entropy and Its Application in Decision-Making for Medical Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fuyuan Xiao ◽  
Xiao-Guang Yue
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Medical Diagnosis ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Numerical Examples ◽  
Open Issue ◽  
Complex Planes ◽  
Complex Valued ◽  
Complex Entropy

In decision-making systems, how to measure uncertain information remains an open issue, especially for information processing modeled on complex planes. In this paper, a new complex entropy is proposed to measure the uncertainty of a complex-valued distribution (CvD). The proposed complex entropy is a generalization of Gini entropy that has a powerful capability to measure uncertainty. In particular, when a CvD reduces to a probability distribution, the complex entropy will degrade into Gini entropy. In addition, the properties of complex entropy, including the nonnegativity, maximum and minimum entropies, and boundedness, are analyzed and discussed. Several numerical examples illuminate the superiority of the newly defined complex entropy. Based on the newly defined complex entropy, a multisource information fusion algorithm for decision-making is developed. Finally, we apply the decision-making algorithm in a medical diagnosis problem to validate its practicability.

scholarly journals Hesitant Fuzzy Generalised Bonferroni Mean Operators Based on Archimedean Copula for Multiple-Attribute Decision-Making

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ju Wu ◽  
Fang Liu ◽  
Yuan Rong ◽  
Yi Liu ◽  
Chengxi Liu
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Information Fusion ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operator ◽  
Archimedean Copula ◽  
Fuzzy Information ◽  
Bonferroni Mean ◽  
Operation Rules ◽  
Multiple Attribute

Information fusion is an important part of multiple-attribute decision-making, and aggregation operator is an important tool of decision information fusion. Integration operators in a variety of fuzzy information environments have a slight lack of consideration for the correlation between variables. Archimedean copula provides information fusion patterns that rely on the intrinsic relevance of information. This paper extends the Archimedean copula to the aggregation of hesitant fuzzy information. Firstly, the Archimedean copula is used to generate the operation rules of the hesitant fuzzy elements. Secondly, the hesitant fuzzy copula Bonferroni mean operator and hesitant fuzzy weighted copula Bonferroni mean operator are propounded, and several properties are proved in detail. Furthermore, a decision-making method based on the operators is proposed, and the specific decision steps are given. Finally, an example is presented to illustrate the practical advantages of the method, and the sensitivity analysis of the decision results with the change of parameters is carried out.

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