基于分类修正的多证据合成方法

鉴于传统冲突量参数无法有效地衡量证据间的相似程度, 提出一种基于分类修正的多证据合成方法, 以解决证据合成中的高冲突悖论和“0”悖论. 首先, 利用证据距离参数、冲突量参数和方向角度参数共同衡量各证据间的相似程度, 将证据分为一致证据、不冲突证据、低冲突证据以及高冲突证据4 类; 然后, 利用3 个参数赋予各类证据不同的修正系数; 最后, 利用Dempster 规则对修正后的证据进行合成. 算例分析表明, 所提出的方法能够较好地解决高冲突悖论和“0”悖论, 而且保留了证据理论优良的数学性质.

Combination method of multi-evidence based on classification correction

Since the traditional conflict parameter can’t measure the degree of similarity between the evidence effectively, a combination method of evidence based on classification correction is proposed to solve the high conflict paradox and “0” paradox. Three parameters are used to measure the degree of similarity between the evidence together, including the evidence distance parameter, the conflict parameter and the direction parameter. According to the three parameters, the evidence is classified into four categories, including the consistent evidence, the no-conflict evidence, the low conflict evidence and the high conflict evidence. The different evidence is given different correction coefficients, and the revised evidence is combined by means of Dempster rule. Numerical experiments show that the proposed method can solve the high conflict paradox and “0” paradox well, and keep the good mathematical properties of evidence theory.