Efficient combination rule of Dezert-Smarandache theory

The Dezert-Smarandache theory （DSmT） is a useful method for dealing with uncertainty problems. It is more efficient in combining conflicting evidence. Therefore, it has been successfully applied in data fusion and object recognition. However, there exist shortcomings in its combination rule. An efficient combination rule is presented, that is, the evidence＇s conflicting probability is distributed to every proposition based on remaining the focal elements of conflict. Experiments show that the new combination rule improves the reliability and rationality of the combination results. Although evidences conflict another one highly, good combination results are also obtained.     

并发故障信息DSmT融合算法的应用研究

针对主冷却泵长期工作在高温、高压及高辐射的恶劣工作环境中,其并发故障存在较高概率并难以诊断,提出一种DSmT决策级融合算法,构建了泵主轴转子不平衡、泵主轴转子不对中及主轴损坏的DSmT辨识理论框架,由实测采集数据综合确定DSmT故障特征信度赋值,利用DSmT对多个独立证据源进行决策判断得出主冷却剂泵故障及并发故障的诊断结果.实验结果表明该方法能有效识别主冷却剂泵并发故障特征,具有一定的理论基础及工程应用价值.     

基于证据数据分类算法的水声目标识别研究

证据分类算法已被广泛应用于模式识别中。针对传统证据近邻算法在证据权重和组合规则上的局限,研究了一种新的基于DSmT的证据K近邻识别算法(DSmT-KNN)。首先在水声目标的各类别训练模板库中,利用目标数据与各近邻的特征相似度来分别构造基本置信指派,并根据K个近邻数据的距离大小对构造的置信指派进行加权。然后利用DSmT规则对加权证据进行融合。最后根据每个类别下融合结果的算术平均值来判断目标的类别属性。通过水声目标实测数据实验,将DSmT-KNN与其他几种常见的方法进行了对比分析,结果表明新算法能有效提高系统的识别准确率。     

修正的DSmT证据编码方法

计算量是影响Dezert Smarandache理论(DSmT)应用的重要因素之一,也一直是DSmT的研究热点。计算编码由于其证据表示和信度计算的合理性,得到了广泛的认可。计算编码在编码转换时简单有效,但在解码转换时需要进行全部可能元素查找,需要大量查找计算,降低了计算编码的执行效率。文章提出了一种修正的DSmT证据〖JP3〗编码方法,该方法将焦元关系转换为唯一的位置属性并事前存储于数据库中,减小了在解码过程中的查找计算量,修正方法在不改变合成结果的前提下更加有效。算例也验证了修正方法在合成结果上的正确性和计算量上的高效性。     

快速mass函数收敛算法

DSmT可以很好地解决高冲突证据的组合问题,但是它存在计算量大和主焦元的mass函数难以收敛的问题.针对这些不足,提出快速mass函数收敛算法,在融合过程中只计算辨识框架中单元素形成焦元所占的基本置信指派,并对其做归一化处理,重构mass函数,大大降低了计算量的同时也使mass函数迅速收敛向规定的阈值,以利于快速准确地...     

New conflict representation model in generalized power space

The study on alternative combination rules in Dempster-Shafer theory (DST) when evidences are in conflict has emerged again recently as an interesting topic, especially in data/information fusion applications. The earlier researches have mainly focused on investigating the alternative which would be appropriate for the conflicting situation, under the assumption that a conflict is identified. However, the current research shows that not only the combination rule but also the classical conflict coefficient in DST are not correct to determine the conflict degree between two pieces of evidences. Most existing methods of measuring conflict do not consider the open world situation, whose frame of discernment is incomplete. To solve this problem, a new conflict representation model to determine the conflict degree between evidences is proposed in the generalized power space, which contains two parameters: the conflict distance and the conflict coefficient of inconsistent evidences. This paper argues that only when the conflict measure value in the new representation model is high, it is safe to say the evidences are in conflict. Experiments illustrate the efficiency of the proposed conflict representation model.