一种基于证据综合权重折扣的加权平均法

A Weighted Averaging Method Based on Overall Weight of Discounted Evidences

针对一类具有不同重要性与可靠度的证据融合问题,将证据静态权重与体现证据间相似性的动态权重结合起来,提出了一种基于证据综合权重折扣的加权平均法.首先,根据证据的先验信息即静态权重以及证据间的相似性,计算经静态权重折扣的各证据与其加权平均的距离而获得的证据的动态权重,再将静态权重与动态权重综合形成证据的综合权重.然后,将综合权重作为折扣因子建立具有信任折扣的证据推理模型.最后,利用加权平均法对修正后的证据进行组合.该方法能有效处理高冲突证据的融合,算例结果验证了所提方法的有效性.

For a class of evidence fusion problems that integrate different importance with credibility, a new weighted average method based on overall weight of discounted evidence is proposed by combining the static weight with the dynamic weight embodying the similarity between evidences. According to the transcendental information, i.e., static weight and the similarity between evidences, the dynamic weight of evidence is acquired by calculating the distances between all evidences discounted via a static weight computation and their weighted average, then the static weight is integrated with dynamic weight to form an overall weight of evidence. An evidential reasoning model is thus developed with some discount, where the overall weight plays the role as a discounting factor. Finally, the modified evidences are combined together by the weighted average method proposed. A numerical example shows the effectiveness of the method in the integration of those evidences which are in sharp conflict with each other.