REGRESSION METHODS FOR HESITANT FUZZY PREFERENCE RELATIONS
提出了两种回归方法，将犹豫模糊偏好关系转化为具有最高一致性或弱一致性的模糊偏好关系，并通过算法和实例验证了方法的实用性和优越性。
In this paper, we develop two regression methods that transform hesitant fuzzy preference relations (HFPRs) into fuzzy preference relations (FPRs). On the basis of the complete consistency, reduced FPRs with the highest consistency levels can be derived from HFPRs. Compared with a straightforward method, this regression method is more efficient in the Matlab environment. Based on the weak consistency, another regression method is developed to transform HFPRs into reduced FPRs which satisfy the weak consistency. Two algorithms are proposed for the two regression methods, and some examples are provided to verify the practicality and superiority of the proposed methods.