On Agent-Specific Fuzzy Entropy Functions
本文提出灵活的模糊和概率模糊熵函数，用于量化主体感知的系统整体模糊性，考虑主体对模糊性的固有态度和夸大/弱化倾向，并通过实际案例展示其意义。
The concern of this article is to introduce versatile fuzzy and probabilistic-fuzzy entropy functions that could quantify the overall ambiguity of a system as perceived by the agent. The central principle behind the proposed functions is: besides the underlying ambiguity, an agent’s inherent outlook toward ambiguity casts a great influence on the perceived entropy. The proposed functions also consider the tendency to exxaggerate/downplay the ambiguity. More specifically, the proposed entropy functions take as argument the perceived ambiguity value, rather than the actual membership grade. Besides, the relationship between the perceived membership grade and ambiguity is closely modeled to compute the overall entropy. The variants of the proposed functions for the probabilistic-fuzzy domain are also presented. A real case study is included to demonstrate the significance of the work.