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A Group Decision Making Method Based on Dempster-Shafer Fuzzy Soft Sets Under Incomplete Information

Yuanxiang Dong, Zhi Xiao
2015 International Journal of Hybrid Information Technology  
This paper proposes a new type of fuzzy soft sets, D-S fuzzy soft sets which combine D-S theory of evidence and fuzzy soft sets.  ...  This paper introduces the concept of Dempster-Shafer fuzzy soft sets combined Dempster-Shafer theory and fuzzy soft sets.  ...  This paper proposed the D-S fuzzy soft sets combining D-S theory and fuzzy soft sets to cope with the group decision making on incomplete fuzzy soft sets.  ... 
doi:10.14257/ijhit.2015.8.3.25 fatcat:gqhvx3b7mzbptas23avxhpqpsu

Subject index to volume 2

1988 International Journal of Approximate Reasoning  
temporal relationships in, 337 Autonomous agents, centrality of, in theories of action under uncertainty, 303-326 Backward chaining, with fuzzy goals and rules, 108 Bayes belief network, construction  ...  activator, 342 Bayesian belief networks, 337 stochastic stimulation of, 331 Bayesian inference, in model-based machine vision, 327-328 Bayesian prediction, for artificial intelligence, 342 Bayesian scheme  ...  within, 349- 364 theory, in automatic object classification, comparison with fuzzy set theory, 105-106 Rule base, using numerical uncertainty repre- sentations, 330-331 Rule-based inference system  ... 
doi:10.1016/0888-613x(88)90114-4 fatcat:rj373wy2pzff3d3otqdhxs2gca

Enterprise Credit Risk Evaluation models: A Review of Current Research Trends

Ming-Chang Lee
2012 International Journal of Computer Applications  
We found that the current research trends are necessary a method for reduction the feature subset, many hybrids SVM based model and rough model are proposed.  ...  This research aims to provide a state-of-the art review of the relative literature and indicate relevant research opportunities.  ...  In combination of two model, [70] used Rough set theory and GA-based SVM to evaluate credit risk.  ... 
doi:10.5120/6311-8643 fatcat:n4gqs53qqzdg7g3hjrzhlmjd5y

Survey of Rough and Fuzzy Hybridization

Pawan Lingras, Richard Jensen
2007 IEEE International Fuzzy Systems conference proceedings  
Since both theories originated in the expert system domain, there are a number of research proposals that combine rough and fuzzy concepts in supervised learning.  ...  This paper provides a broad overview of logical and black box approaches to fuzzy and rough hybridization.  ...  Section V discusses the unsupervised learning based on combination of rough and fuzzy set theories.  ... 
doi:10.1109/fuzzy.2007.4295352 dblp:conf/fuzzIEEE/LingrasJ07 fatcat:vygojaq46zb6zej6q3jtlna5au

Harmony Search Algorithm and Fuzzy Logic Theory: An Extensive Review from Theory to Applications

Mohammad Nasir, Ali Sadollah, Przemyslaw Grzegorzewski, Jin Hee Yoon, Zong Woo Geem
2021 Mathematics  
and dynamic adaptation of the HS parameters based on human perception on the other hand, can be a powerful combination for solving optimization problems.  ...  FL theory is a mathematical approach to expressing uncertainty by applying the conceptualization of fuzziness in a system.  ...  The different ways of communicating uncertainty are FL and probability. The idea of fuzzy set inclusion was used by fuzzy set theory, while probability theory uses the idea of empirical probability.  ... 
doi:10.3390/math9212665 fatcat:qkon5rx4xbfdhnoii5shbktxea

Fuzzy-probabilistic calculations of water-balance uncertainty

Boris Faybishenko
2010 Stochastic environmental research and risk assessment (Print)  
The objective of this paper is to present the theory for, and a case study as an application of, the fuzzyprobabilistic approach, combining probability and possibility theory for simulating soil water  ...  Instead, predictions and uncertainty analysis can be made using uncertain input parameters expressed as probability boxes, intervals, and fuzzy numbers.  ...  The objective of this paper is to present the theory and application (through a case study) of the fuzzy-probabilistic approach for assessing the uncertainty involved in hydrogeological modeling, based  ... 
doi:10.1007/s00477-010-0379-y fatcat:xtp6es66gbg6hlpzcjwwev3mrm

Dependent-Chance Goal Programming for Water Resources Management under Uncertainty

Haiying Guo, Honghua Shi, Xiaosheng Wang
2016 Scientific Programming  
And a dependent-chance goal programming (DCGP) model is provided for water resources management under such circumstance.  ...  In the end, the developed model is applied to a numerical example to illustrate the effectiveness of the model.  ...  For example, Jairaj and Vedula [9] formulated a fuzzy mathematical programming model based on fuzzy set theory for a multireservoir system. Sahoo et al.  ... 
doi:10.1155/2016/1747425 fatcat:yuc7sss55bhp7i3dhz6h7wstbm

An Efficient Neuro-Fuzzy-Genetics Approach for Multi Criteria Decision Making

Chandrasekhar Mesh Ram, Shyam Sundar Agrawal
2015 International Journal of Hybrid Information Technology  
The paper begins with introduction and literature review followed by some fundamental of fuzzy set theory, neural network and genetics algorithm and methodology to apply them in multi criteria decision-making  ...  The present paper, we applied combined neural network, fuzzy logic and genetics algorithm approach to multi criteria decision-making in different areas.  ...  Yeh and Lee [8] showed the application of Neuro-Fuzzy hybrid modeling.  ... 
doi:10.14257/ijhit.2015.8.5.29 fatcat:ci363m2te5cp3mjarha6iedrxy

Edge covering problem under hybrid uncertain environments

Yaodong Ni
2013 Applied Mathematics and Computation  
We propose three decision models and present a hybrid intelligent algorithm to solve the proposed models where genetic algorithm and random fuzzy simulation are embedded.  ...  Since various types of uncertainty always exist in real world, this paper considers ECP under hybrid uncertain environments where randomness and fuzziness coexist.  ...  Preliminary knowledge of random fuzzy theory In this section, we introduce some basic knowledge of random fuzzy theory. Random fuzzy theory is based on both probability theory and credibility theory.  ... 
doi:10.1016/j.amc.2012.11.096 fatcat:tns7djnfcraxbpig42jf766sei

Development of a hybrid adaptive neuro-fuzzy system for the prediction of sediment transport [chapter]

C Dancey, P Diplas, A Celik, T Akar, M Valyrakis
2006 River Flow 2006  
Peculiar features of development of hybrid adaptive systems using neuro-fuzzy network structures are discussed. Quality and amount of information about an object is insufficient.  ...  Software significantly simplifies development and investigation of proprietary architectures of neuro-fuzzy networks, genetic algorithms.  ...  Burhan, 1998. increases efficiency of network settings, namely, Development of A Systematic Methodology of Fuzzy decreases probability of entering into local domains of Logic Modeling// IEEE Transactions  ... 
doi:10.1201/9781439833865.ch92 fatcat:jg4objdjynf6hiaiiybt5oq4ye

An overview of rough-hybrid approaches in image processing

Aboul Ella Hassanien, Ajith Abraham, James F. Peters, Gerald Schaefer
2008 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)  
, and the classification of objects.  ...  In this paper we show how rough sets have been combined with various other methodologies such as neural networks, wavelets, mathematical morphology, fuzzy sets, genetic algorithms, bayesian approaches,  ...  [35] presented a multi-scale method based on the hybrid notion of rough fuzzy sets, coming from the combination of two models of uncertainty like vagueness by handling rough sets and coarseness by handling  ... 
doi:10.1109/fuzzy.2008.4630665 dblp:conf/fuzzIEEE/HassanienAPS08 fatcat:yyjkgiklkjbevcwl42crlzh5ee

Adaptive Neuro-Fuzzy Inference System for generating scenarios in business strategic planning

Sorousha Moayer, Parisa A. Bahri, Ali Nooraii
2007 2007 IEEE International Conference on Systems, Man and Cybernetics  
The proposed methodology includes: (1) defining the scope and internal and external variables (2) determining rules from experts (3) preparing ANFIS system and (4) generating probable scenarios based on  ...  This study incorporates the concepts of neural network and fuzzy logic.  ...  It is built based on a hybrid technique: a combination of ANNs and fuzzy logic.  ... 
doi:10.1109/icsmc.2007.4413586 dblp:conf/smc/MoayerBN07 fatcat:x5ps5pmivfcmbhwdgbqwduqasm

Fuzzy Random Chance-Constrained Programming Model for the Vehicle Routing Problem of Hazardous Materials Transportation

Liying Zhao, Ningbo Cao
2020 Symmetry  
To settle the above models, a hybrid intelligent algorithm was designed, which was a combination of genetic algorithm and fuzzy random simulation algorithm, which simultaneously proved its convergence.  ...  Meanwhile, accident probability and vehicle speed were set to be stochastic.  ...  Two instances of model-orientation were figured out by hybrid intelligence algorithm, which combined genetic algorithm and fuzzy random simulation algorithm.  ... 
doi:10.3390/sym12081208 fatcat:nhvygxrptzczhhvootpfnawac4

A New Hybrid Intelligent Algorithm for Fuzzy Multiobjective Programming Problem Based on Credibility Theory

Zu-Tong Wang, Jian-Sheng Guo, Ming-Fa Zheng, Ying Wang
2014 Mathematical Problems in Engineering  
For solving the fuzzy MOP problem efficiently, Latin hypercube sampling, fuzzy simulation, support vector machine, and artificial bee colony algorithm are integrated to build a hybrid intelligent algorithm  ...  Based on the credibility theory, this paper is devoted to the fuzzy multiobjective programming problem.  ...  Acknowledgments This work was supported by the National Natural Science Foundation under Grant no. 71171199 and the Natural Science Foundation of Shaanxi Province under Grant no. 2013JM1003.  ... 
doi:10.1155/2014/909203 fatcat:vz4eq42alffzhbnn3wpedy5w4i

Intelligent Evolutionary Algorithm for Fuzzy Programming Based on Nonlinear Support Vector Machine

2017 Revista Técnica de la Facultad de Ingeniería Universidad del Zulia  
In order to overcome this defect, this paper presents a hybrid intelligent evolutionary algorithm based on nonlinear support vector machine (SVM) to solve the fuzzy programming problem.  ...  Finally, the fuzzy programming model based on the improved intelligent evolutionary algorithm is fitted and optimized by nonlinear support vector machine.  ...  Then, by comparing the membership of fuzzy object set and fuzzy constraint set, the optimal membership of the intersection of the two sets is obtained.  ... 
doi:10.21311/ fatcat:ns54ijbztnb55gbuyqlpezxdfu
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