Fuzziness and randomness in an optimization framework
Fuzzy Sets and Systems
The central limit theorems for fuzzy random variables
Information Sciences—Informatics and Computer Science: An International Journal
The law of large numbers for fuzzy numbers with unbounded supports
Fuzzy Sets and Systems - Special issue on fuzzy numbers and uncertainty
Kolmogorov's strong law of large numbers for fuzzy random variables
Fuzzy Sets and Systems
On minimum-risk problems in fuzzy random decision systems
Computers and Operations Research
Probabilistic foundations for measurement modelling with fuzzy random variables
Fuzzy Sets and Systems
Strong law of large numbers for t-normed arithmetics
Fuzzy Sets and Systems
On the representation of fuzzy rules
International Journal of Approximate Reasoning
Fuzzy random delayed renewal process and fuzzy random equilibrium renewal process
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A generalization of additive generator of triangular norms
International Journal of Approximate Reasoning
The modes of convergence in the approximation of fuzzy random optimization problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Uncertainty Analysis and Decision Making; Guest Editors: Yan-Kui Liu, Baoding Liu, Jinwu Gao
Fuzzy random renewal process with queueing applications
Computers & Mathematics with Applications
Fuzzy random renewal reward process and its applications
Information Sciences: an International Journal
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
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The objective of this paper is to derive some limit theorems of fuzzy random variables under the extension principle associated with continuous Archimedean triangular norms (t-norms). First of all, some convergence theorems for the sum of fuzzy random variables in chance measure and expected value are proved respectively based on the arithmetics of continuous Archimedean triangular norms. Then, a law of large numbers for fuzzy random variables is established by using the obtained convergence theorems. The results of the derived law of large numbers can degenerate to the strong laws of large numbers for random variables and fuzzy variables, respectively.