Distribution of mathematical software via electronic mail
Communications of the ACM
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Membership function as an evaluation
Fuzzy Sets and Systems
Curve and surface fitting with splines
Curve and surface fitting with splines
Constructing membership functions using interpolation and measurement theory
Fuzzy Sets and Systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
On the issue of obtaining OWA operator weights
Fuzzy Sets and Systems
Definition of general aggregation operators through similarity relations
Fuzzy Sets and Systems
Algorithm 587: Two Algorithms for the Linearly Constrained Least Squares Problem
ACM Transactions on Mathematical Software (TOMS)
Logical Structures for Representation of Knowledge and Uncertainty
Logical Structures for Representation of Knowledge and Uncertainty
Including importances in OWA aggregations using fuzzy systems modeling
IEEE Transactions on Fuzzy Systems
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A good choice of membership functions and aggregation operators is crucial to the behavior fuzzy systems. In many cases there are no theoretical criteria that would justify the use of one or another function, and they are selected based on their goodness of fit to empirical data. This paper discusses a general non-parametric approach to construction of membership functions and aggregation operators based on empirical data. This method is computer oriented: it does not produce operators in closed algebraic form, but the quality of fit and flexibility are superior to other methods. The method is also general, since it can produce membership functions and operators from any class. Restrictions to a particular class can be easily introduced. Examples based on published empirical data are provided.