The application of level-2 fuzzy sets in fuzzy and uncertain data modeling

2002 ◽  
Author(s):  
G. De Tre ◽  
R. De Caluwe ◽  
A. Verkeyn
Keyword(s):  
Fuzzy Sets ◽  
Uncertain Data ◽  
Data Modeling ◽  
Level 2

An interval type-2 hesitant fuzzy best-worst method

2021 ◽  
pp. 1-28
Author(s):  
Ashraf Norouzi ◽  
Hossein Razavi hajiagha
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Fuzzy Ahp ◽  
Uncertain Data ◽  
Interval Type ◽  
Best Worst Method

Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension.

Data Discovery Approaches for Vague Spatial Data

Data Mining ◽  
2013 ◽  
pp. 50-65
Author(s):  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Rough Set ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Spatial Databases ◽  
Uncertain Data ◽  
Rule Extraction ◽  
Data Discovery

This chapter focuses on the application of the discovery of association rules in approaches vague spatial databases. The background of data mining and uncertainty representations using rough set and fuzzy set techniques is provided. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets is described. Finally, an example of rule extraction for both types of uncertainty representations is given.

scholarly journals Querying Uncertain Data in Geospatial Object-relational Databases Using SQL and Fuzzy Sets

2013 ◽  
Vol 21 (4) ◽  
pp. 1-12 ◽  
Author(s):  
R. Ďuračiová
Keyword(s):  
Fuzzy Sets ◽  
Spatial Data ◽  
Relational Databases ◽  
Spatial Databases ◽  
Query Language ◽  
Uncertain Data ◽  
Uncertainty Modeling ◽  
Geographic Information ◽  
Object Relational ◽  
Efficient Processing

Abstract This paper deals with uncertainty modeling in spatial object-relational databases by the use of Structured Query Language (SQL). The fundamental principles of uncertainty modeling by fuzzy sets are applied in the area of geographic information systems (GIS) and spatial databases. A spatial database system includes types of spatial data and implements the spatial extension of SQL. The implementation of the principles of fuzzy logic to spatial databases brings an opportunity for the efficient processing of uncertain data, which is important, especially when using various data sources (e.g., multi-criteria decision making (MCDM) on the basis of heterogeneous spatial data resources). The modeling and data processing of uncertainties are presented in relation to the applicable International Organization for Standardization (ISO) standards (standards of the series 19100 Geographic information) and the relevant specifications of the Open Geospatial Consortium (OGC). The fuzzy spatial query approach is applied and tested on a case study with a fundamental database for GIS in Slovakia.

scholarly journals Fuzzy Estimation Of Activities Duration In Construction Projects

2015 ◽  
Vol 61 (2) ◽  
pp. 23-34 ◽  
Author(s):  
N. Ibadov
Keyword(s):  
Fuzzy Sets ◽  
Construction Projects ◽  
Uncertain Data ◽  
Construction Materials ◽  
Construction Equipment ◽  
Planning Phase ◽  
Planning And Management ◽  
Construction Works ◽  
Theory Of Fuzzy Sets

AbstractDuring implementation of construction projects, durations of activities are affected by various factors. Because of this, both during the planning phase of the project as well as the construction phase, managers try to estimate, or predict, the length of any delays that may occur. Such estimates allow for the ability to take appropriate action in terms of planning and management during the execution of construction works. This paper presents the use of the non-deterministic concept for describing the uncertainty of estimating works duration. The concept uses the theory of fuzzy sets. The author describes a method for fuzzy estimations of construction works duration based on the fact that uncertain data is an inherent factor in the conditions of construction projects. An example application of the method is presented. The author shows a fuzzy estimation for the duration of an activity, taking into consideration the distorting influence caused by malfunctioning construction equipment and delivery delays of construction materials.

scholarly journals A bad data detection approach to EPS state estimation based on fuzzy sets and wavelet analysis

2020 ◽  
Vol 216 ◽  
pp. 01029
Author(s):  
Irina Kolosok ◽  
Liudmila Gurina
Keyword(s):  
Wavelet Analysis ◽  
Fuzzy Sets ◽  
State Estimation ◽  
Uncertain Data ◽  
Data Detection ◽  
Bad Data Detection ◽  
Combined Application ◽  
Measurement Validation ◽  
Incomplete And Uncertain Data ◽  
Bad Data

The paper offers an algorithm for detection of erroneous measurements (bad data) that occur at cyberattacks against systems for data acquisition, processing and transfer and cannot be detected by conventional methods of measurement validation at EPS state estimation. Combined application of wavelet analysis and theory of fuzzy sets when processing the SCADA and WAMS measurements produces higher accuracy of the estimates obtained under incomplete and uncertain data and demonstrates the efficiency of proposed approach for practical computations in simulated cyberattacks.

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