Probability-Based Fuzzy Sets: Extensions and Applications

Dear Colleagues,

Since the concept of fuzzy sets was proposed by Zadeh in 1965, it has become possible to describe uncertainty and fuzziness information in practice quantitatively. Meanwhile, to reduce the loss of processed information, the probability theory is introduced into fuzzy sets to develop probabilistic fuzzy sets, hesitant probabilistic fuzzy sets, probabilistic intuitionistic fuzzy sets, etc. Their applications include probabilistic fuzzy clustering, probabilistic fuzzy image fusion, probabilistic fuzzy neural network control, and probabilistic fuzzy decision making. With the rapid development of artificial intelligence technology and tools, our understanding of the real world has been further deepened. Accordingly, researchers have successively defined various fuzzy set extensions to depict phenomena that are more complex in reality, such as the picture fuzzy sets, q‐rung orthopair fuzzy sets, T-spherical fuzzy sets, etc. Therefore, meaningfully introducing probability theory into these new extensions to enhance the accuracy of information expression becomes an intriguing and important issue. Especially research areas such as the definitions of novel forms of probability-based fuzzy sets, probability-based fuzzy system optimization, corresponding operational rules, distance measurement, information aggregation, and their applications in various fields need further investigation.

This Special Issue welcomes papers combining probability theory with recent, new concepts in fuzzy sets to address the uncertainty and complexity that people are encountering.

Prof. Dr. Zaoli Yang
Prof. Dr. Shaomin Wu
Prof. Dr. Yi Su
Guest Editors

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