scholarly journals A Soft Rough-Fuzzy Preference Set-Based Evaluation Method for High-Speed Train Operation Diagrams

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Dingjun Chen ◽  
Shaoquan Ni ◽  
Chang’an Xu ◽  
Hongxia Lv ◽  
Keyun Qin
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
High Speed ◽  
Evaluation Method ◽  
Attribute Reduction ◽  
Track Maintenance ◽  
Study Results ◽  
Train Operation ◽  
Overall Evaluation

This paper proposes a method of high-speed railway train operation diagram evaluation based on preferences of locomotive operation, track maintenance, S & C, vehicles and other railway departments, and customer preferences. The application of rough set-based attribute reduction obtains the important relative indicators by eliminating excessive and redundant evaluation indicators. Soft fuzzy set theory is introduced for the overall evaluation of train operation diagrams. Each expert utilizes a set of indicators during evaluation based on personal preference. In addition, soft fuzzy set theory is applied to integrate the information obtained via expert evaluation in order to obtain an overall evaluation. The proposed method was validated by a case study. Results demonstrate that the proposed method flexibly expresses the subjective judgments of experts while effectively and reasonably handling the uncertainty of information, which is consistent with the judgment process of humans. The proposed method is also applicable to the evaluation of train operation schemes which consist of multiple diagrams.

Granular Computing Based Data Mining in the Views of Rough Set and Fuzzy Set

2010 ◽  
pp. 148-178 ◽  
Author(s):  
Guoyin Wang ◽  
Jun Hu ◽  
Qinghua Zhang ◽  
Xianquan Liu ◽  
Jiaqing Zhou
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Rule Sets ◽  
Incomplete Information Systems

Granular computing (GrC) is a label of theories, methodologies, techniques, and tools that make use of granules in the process of problem solving. The philosophy of granular computing has appeared in many fields, and it is likely playing a more and more important role in data mining. Rough set theory and fuzzy set theory, as two very important paradigms of granular computing, are often used to process vague information in data mining. In this chapter, based on the opinion of data is also a format for knowledge representation, a new understanding for data mining, domain-oriented data-driven data mining (3DM), is introduced at first. Its key idea is that data mining is a process of knowledge transformation. Then, the relationship of 3DM and GrC, especially from the view of rough set and fuzzy set, is discussed. Finally, some examples are used to illustrate how to solve real problems in data mining using granular computing. Combining rough set theory and fuzzy set theory, a flexible way for processing incomplete information systems is introduced firstly. Then, the uncertainty measure of covering based rough set is studied by converting a covering into a partition using an equivalence domain relation. Thirdly, a high efficient attribute reduction algorithm is developed by translating set operation of granules into logical operation of bit strings with bitmap technology. Finally, two rule generation algorithms are introduced, and experiment results show that the rule sets generated by these two algorithms are simpler than other similar algorithms.

Identification of unbalanced bids based on grey-fuzzy evaluation method

2020 ◽  
Vol 47 (3) ◽  
pp. 272-278
Author(s):  
Limin Su ◽  
Huimin Li ◽  
Zhangmiao Li ◽  
Yongchao Cao
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Evaluation Method ◽  
Unit Price ◽  
Fuzzy Evaluation ◽  
Analytic Hierarchy ◽  
Identification Method ◽  
Score Vector ◽  
Grey Relational

To provide theoretical reference for owners to identify unbalanced bids, this paper aims to construct an identification method based on grey relational and fuzzy set theory. Firstly, to measure the closeness degree between bidding unit price from engineering’s estimated price, grey relational analysis theory is used to express the relationship between them. Secondly, a combined weight method determining all line items is calculated through integrating analytic hierarchy model and maximizing deviation method. Thirdly, based on fuzzy set theory, the membership degree and the fuzzy relation matrix are constructed, and then a fuzzy comprehensive identification method is established to identify unbalanced bidding. Fourthly, on the basis of fuzzy comprehensive identification method, the scoring set and total score vector are designed, and the rank of unbalanced bids is obtained by total score vector. Finally, a practical construction project bidding is stated to illustrate the effectiveness and practicability of the proposed method.

A psychological evaluation method for fluctuating random noise based on fuzzy set theory

1995 ◽  
Vol 45 (2) ◽  
pp. 139-154 ◽  
Author(s):  
Shizuma Yamaguchi ◽  
Yuichi Kato ◽  
Kensei Oimatsu ◽  
Tetsuro Saeki
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Evaluation Method ◽  
Random Noise ◽  
Psychological Evaluation

Evaluate Engineering Project Risk Based on Fuzzy Set

2011 ◽  
Vol 368-373 ◽  
pp. 2216-2219
Author(s):  
Wei Zhang ◽  
Leng Fei Sun
Keyword(s):  
Risk Factors ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Risk Evaluation ◽  
Evaluation Method ◽  
Fuzzy Numbers ◽  
Engineering Project ◽  
Project Risk ◽  
Engineering Projects

Engineering projects often face risks from technology, economy, nature, social and other aspects. Risk factors are of interdependence and interaction, so they are very difficult to be quantified. The paper describes a risk evaluation method for engineering projects based on fuzzy set theory which uses respective fuzzy numbers to evaluate the factors. The primary weights of factors and evaluation of alternatives are determined by applying fuzzy numbers. The results are consistent with the results calculated by conventional risk evaluation method. The research demonstrates that the method is objective and accurate, and is of an application value in the risk evaluation for engineering projects.

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