Research on Two-Stage Hesitate Fuzzy Information Fusion Framework Incorporating Prospect Theory and Dichotomy Algorithm

Abstract

In order to control the systematic divergence among decision makers (DMs) and preserve the original decision preference, this paper proposes a novel decision information fusion framework under the hesitant fuzzy environment. First, a maximum compactness-based normalization method is presented to normalize hesitant fuzzy elements (HFEs) as pretreatment of decision data. Second, prospect theory is introduced to assign the optimal aggregation weights to maximize the efficiency of the preference aggregation process, in which the expected consensus threshold is viewed as a reference point estimated through statistic inference to distinguish DMs’ status. Third, an effective feedback mechanism is designed to improve group consensus, and the dichotomy algorithm is utilized to search optimal feedback weight to preserve original decision information. Finally, a case study and comparison analysis are illustrated to show the efficiency of the proposed hesitant fuzzy information fusion method.

Research on Multi-Source Heterogeneous Big Data Fusion Method Based on Feature Level

“…Moreover, due to the high professional characteristics of the data adopted, the amount of data depends on the sensor dependence, and the fault tolerance is high, but it has a high initial decisionmaking operation cost. The main technologies used in this kind of fusion include fuzzy reasoning theory [19], expert system [20], and so on.…”