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

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 existing methods may cause counter-intuitive results, which can bring extra heavy computational burden due to nonlinear optimization model, and lose good property associativity commutativity in Dempster-Shafer theory (DST). To address these problems, a novel IBSs method named CSUI (conflict, similarity, uncertainty, intuitionistic fuzzy sets)-DST proposed by introducing similarity measurement measure degree conflict among uncertainty discord, non-specificity fuzziness IBSs. Considering two measures at same time, weight each IBS determined according modified reliability degree. From perspective sets, we propose weighted average rule addition number multiplication operators. The effectiveness rationality this are validated numerical examples application target recognition.