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Investigates similarity measures in continuous probabilistic hesitant fuzzy sets, highlighting their application in stock investment value pattern recognition.
From the point of view of accuracy of processing the uncertain information in the form of interval-values and simplicity of integration method of hesitant fuzzy information, the continuous probabilistic hesitant fuzzy set (CPHFS) is more effective than other fuzzy sets. This study aims to investigate the construction of similarity measures of continuous probabilistic hesitant fuzzy elements (CPHFEs) and CPHFSs based on distance and implication function. Two kinds of similarity measures of (CPHFEs) and CPHFSs are presented. Furthermore, their properties and relationships with each other are studied and discussed. Finally, an example of the pattern recognition of stock investment value is given to apply the above method to practical problems.
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Rong et al. (2026) studied this question.
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