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Fuzzy Sets and Systems
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Approximation of fuzzy numbers by trapezoidal fuzzy numbers preserving the expected interval
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International Journal of Advanced Intelligence Paradigms
Simulation of fuzzy random variables
Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Applied Soft Computing
Train timetable problem on a single-line railway with fuzzy passenger demand
IEEE Transactions on Fuzzy Systems
Trapezoidal approximations of fuzzy numbers
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Approximations of fuzzy numbers by trapezoidal fuzzy numbers preserving the ambiguity and value
Computers & Mathematics with Applications
Change a sequence into a fuzzy number
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
A multi-level ant-colony mining algorithm for membership functions
Information Sciences: an International Journal
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Information Sciences: an International Journal
On elicitation of membership functions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
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Information Sciences: an International Journal
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This paper focuses on modelling fuzzy numbers with meaningful membership functions. More precisely, it proposes a method to construct trapezoidal fuzzy number approximations from raw discrete data. In many applications, input information is numerical, and therefore, particular fuzzy sets, such as fuzzy numbers, hold great interest and relevance in managing data imprecision and vagueness. The proposed technique provides an efficient way to obtain trapezoidal numbers using linear regression. The technique is simple, fast, and effective. Preliminary tests are performed using different types of input data: a Gaussian function, a Sigmoidal function, three datasets of synthetic discrete data, and an histogram obtained from a colour satellite image.