A parametric representation of fuzzy numbers and their arithmetic operators
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
Some operations on intuitionistic fuzzy sets
Fuzzy Sets and Systems
Fractional programming approach to fuzzy weighted average
Fuzzy Sets and Systems
Data Envelopment Analysis: Theory, Methodology and Application
Data Envelopment Analysis: Theory, Methodology and Application
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Network flow problems with fuzzy arc lengths
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Comparison of Discrete Algorithms for Fuzzy Weighted Average
IEEE Transactions on Fuzzy Systems
A Multicriteria Approach to Data Summarization Using Concept Ontologies
IEEE Transactions on Fuzzy Systems
Aggregation Using the Linguistic Weighted Average and Interval Type-2 Fuzzy Sets
IEEE Transactions on Fuzzy Systems
Aggregation Using the Fuzzy Weighted Average as Computed by the Karnik–Mendel Algorithms
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
Data standardization is a basic task in data analysis when several incommensurable criteria are involved. This paper discusses a data-standardization method for a set of fuzzy numbers that subtracts their minimum from the number to be standardized and divides the result by the difference between their maximum and minimum. Two approaches, i.e., membership grade and α-cut, are proposed, and associated models are developed based on the extension principle. Both models are nonlinear mathematical programs and, thus, to solve them directly, does not guarantee global optimal solutions. Since the feasible region of the program associated with the α-cut approach is an n-dimensional rectangular parallelepiped, a corner-point method is designed to find the solution. Because of the properties possessed by the standardization formula, the solution obtained from the corner-point method is guaranteed to be optimal. Two examples with different types of fuzzy number show the difficulties encountered to solve thf nonlinear programs directly and demonstrate how to standardize fuzzy numbers by applying the corner-point method.