Abstract
Increasingly complex system environments have put forward higher requirements for information fusion. Under this background, the application prospects of evidence theory which can function well in uncertain information representation and information fusion is promising. The effect of evidence theory on information fusion is closely related to evidence distance measurement method and evidence combination rules. Considering the difference of evidence information in belief level and that in decision level, this paper redefines the evidence distance and puts forward the measurement method of evidence credibility, thus realizing the correction of the original basic probability assignment function (BPA). Based on the evidence credibility, the evidence weight is generated, which leads to an efficient distribution of evidence conflict in the process of evidence combination. So, a new evidence combination rule is generated. According to a series of examples, the evidence distance proposed in this paper can effectively measure the evidence differences, and the new combination rules can reasonably distribute evidence conflicts thus avoiding evidence paradox. At the end of this paper, the new evidence distance and combination rule, combing with the Transferable Belief Model (TBM), are used to fuse the multi-source reliability test information, then the life cycle evaluation of a product is obtained. In addition, through the comparative analysis of the results, the feasibility and validity of this method being applied to practice are verified.
Highlights
As an important means of understanding and explaining the law of nature, uncertainty analysis aims to provide the decision-maker with comprehensive and clear prospective information by characterizing the uncertainties of objective thing
Because of its advantage in decision making under uncertainty, evidence theory has been developed to address the problem of information fusion in complex environment, especially to evaluate the uncertainty based on limited information
In this paper, based on the transferable belief model, the evidence distance is defined by considering both the evidence difference in the credal layer and that in the pignistic layer, and the sum of the distances among the evidences are calculated
Summary
As an important means of understanding and explaining the law of nature, uncertainty analysis aims to provide the decision-maker with comprehensive and clear prospective information by characterizing the uncertainties of objective thing. Belief and plausibility are two of the most important measures in the evidence theory, and closely related to the basic probability assignment function They respectively represent the lower bound and upper bound of the evidence credibility toward some proposition, which means the interval of credibility is (Bel, Pl). Differing from Dempster combination rule, Yager proposes to directly assign evidence conflict to the whole identification framework. Compared with Dempster combination rule, this disjunctive rule assigns the mass value product of the focal elements whose intersection is empty to the union of the two focal elements, avoiding the occurrence of evidence conflict. The disjunctive rule of Dubois successfully avoids evidence conflict after combination, it undoubtedly leads to the increase of the uncertainty of the fusion result like. In order to avoid the waste of information contained in the evidence conflict, some methods should be taken to assign the evidence conflict reasonably and effectively, so as to get more accurate fusion results
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