Abstract
Addressing the problem of fusing highly conflicting evidences in Dempster–Shafer theory is one of the most necessary, important, and difficult research directions all the time, and so far we have published two papers related to it. In this paper, another novel method to handle conflict when combining evidences is proposed, where evidence distance, evidence angle, and improved entropy function, three key tools, are used for constructing the final weight of each body of evidence. This newly proposed approach mainly consists of three steps: firstly, both evidence distance and evidence angle determine the initial weight together; secondly, making use of the improved entropy modifies the initial weight to get the final weight; lastly, the classical D-S combination rule will be applied to obtain final fusion results. Still a classical numeric example and a real fault diagnosis application both demonstrate its effectiveness and efficiency, and compared with other current popular methods including two of our previous works, this new approach can converge fast and reduce most uncertainty of decision-making when fusing highly conflicting evidences.
Highlights
In practical applications, most information is collected by sensors
E remaining paper is organized as follows: Section 2 starts with preliminaries about D-S theory, evidence distance, evidence angle, and improved belief entropy; Section 3 shows the proposed method in detail; Sections 4 and 5 give a numerical example and an application in faulty diagnosis to demonstrate its effectiveness and the efficiency; In the end, a short conclusion is made
After normalizing the support degree of each bodies of evidence (BOEs), we can get its own initial weight iw(mi), which is determined by both the evidence distance and evidence angle function, as shown in the following equation: iw mi njs u1spupmim j
Summary
Most information is collected by sensors. Because of the complexity of the target, the data provided by only one sensor may not be comprehensive enough to reflect the fact. erefore multisensors are needed to produce more data for data fusion. Shafer used the coefficient k to measure the conflict degree between the evidences [10], and in 2000 [33], Murphy presented a simple averaging approach, where the arithmetic average of n evidences is calculated and the Dempster’s combination rule is utilized for fusion. This idea is not reasonable at all in practice because all BOEs can not be seen important. E remaining paper is organized as follows: Section 2 starts with preliminaries about D-S theory, evidence distance, evidence angle, and improved belief entropy; Section 3 shows the proposed method in detail; Sections 4 and 5 give a numerical example and an application in faulty diagnosis to demonstrate its effectiveness and the efficiency; In the end, a short conclusion is made
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