Evidence reasoning for temporal uncertain information based on relative reliability evaluation. (15th December 2018)

• Record Type: Journal Article
• Authors: Fan, Cheng-li; Song, Yafei; Lei, Lei; Wang, Xiaodan; Bai, Shi
• Is Part Of: Expert systems with applications. Volume 113(2018)
• Page Start: 264
• Page End: 276
• Publication Date: 2018-12-15
• Subjects: Dempster–Shafer evidence theory -- Intuitionistic fuzzy sets -- Temporal information fusion -- Reliability evaluation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.06.048
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Relative reliability factor of temporal evidence source is defined. ERE-IFMCDM method is applied to evaluate the reliability factor of temporal BPA. Composite reliability of temporal BPA is constructed. Temporal evidence combination based on composite reliability is put forward. Abstract: As an important tool for information fusion, evidence theory has been widely applied in many areas. Unlike spatial evidence combination, temporal evidence combination is sequential and dynamic, which calls for new temporal evidence combination rule. In this paper, temporal evidence is combined based on relative reliability evaluation and evidence discounting. We first recall the method of evidence reliability evaluation based on intuitionistic fuzzy multiple criteria decision making (ERE-IFMCDM). Based on ERE-IFMCDM method, relative reliability factors of evidence sources in neighboring time nodes can be obtained. Then, according to evidence discounting and Dempster's combination rule, a method for temporal evidence combination based on the relative reliability factor is developed. Numerical examples and simulation demonstrate that the proposed method is time sensitive, which can reflect the dynamic feature of temporal information fusion. Moreover, the proposed method can deal with conflict in temporal fusion much better, which can enhance the anti-interference performance of the fusion system. The fusion framework proposed in this paper provides a dynamic perspective of the fusion of temporal uncertain information. It is helpful for the structural design of intelligent fusion system based on multiple temporal-spatial information.