Combination for Conflicting Interval‐Valued Belief Structures with CSUI‐DST Method

Abstract

Since the basic probability of an interval‐valued belief structure (IBS) is assigned as interval number, its combination becomes difficult. Especially, when dealing with highly conflicting IBSs, most of the existing combination methods may cause counter‐intuitive results, which can bring extra heavy computational burden due to nonlinear optimization model, and lose the good property of associativity and commutativity in Dempster‐Shafer theory (DST). To address these problems, a novel conflicting IBSs combination method named CSUI (conflict, similarity, uncertainty, intuitionistic fuzzy sets)‐DST method is proposed by introducing a similarity measurement to measure the degree of conflict among IBSs, and an uncertainty measurement to measure the degree of discord, non‐specificity and fuzziness of IBSs. Considering these two measures at the same time, the weight of each IBS is determined according to the modified reliability degree. From the perspective of intuitionistic fuzzy sets, we propose the weighted average IBSs combination rule by the addition and number multiplication operators. The effectiveness and rationality of this combination method are validated with two numerical examples and its application in target recognition.

Heterogeneous Information Fusion Recognition Method Based on Belief Rule Structure

“…Many fuzzy rule systems that use training data or expert knowledge to deal with classification problems have been proposed [13]. Belief function theory, also known as Dempster-Shafer theory [14,15], is a very powerful uncertainty modeling and reasoning framework first proposed by Dempster and later promoted by Shafer [16,17]. Yang et al extended fuzzy rules under the framework of belief function theory and proposed belief rules [18], proposed a new knowledge expression, which has been applied in risk assessment, fault diagnosis and other fields.…”