一种快速分层递阶DSmT近似推理融合方法(A)

 本文提出了一种分层递阶的DSmT快速近似推理融合方法,该方法针对超幂集空间中仅单子焦元具有信度赋值的情况,利用二叉树或三叉树分组技术对其刚性分组,与此同时,对每个信息源对应的各个分组焦元进行信度赋值求和,以便实现细粒度超幂集空间向粗粒度超幂集空间映射.然后运用DSmT组合规则和比例冲突分配规则对粗化超幂集空间的两个信息源进行融合,保存该融合结果作为父子之间节点连接权值,然后对每个分组焦元信度赋值归一化处理,通过设定树的深度,来确定分层递阶的次数.最后通过从多个角度比较新、老方法,从而充分地验证了新方法的优越性.     

基于Haenni方法的证据冲突原因分析

由于Dempster组合规则所存在的不足,在组合冲突信息时,会得出与直觉相反的结论。针对Zadeh悖论,Haenni给出了反驳,但冲突产生的原因分析不够全面。鉴于此,本文在他们的基础上,总结出冲突产生的原因,它不仅与传感器受干扰有关,与识别框架不完整也有关系,其中识别框架不完整还包括框架中的元素不详尽,出现了新目标和在两两证据进行推理过程中目标丢失导致的潜在冲突。     

自抗扰控制技术在高低温环境模拟系统中的应用

针对现有健康状态评估方法主观性较强,准确度不高等问题,提出一种基于DSmT理论和模糊综合评判的健康状态评估模型。首先,确定评估对象的评价指标体系,对采集的原始数据进行预处理;然后利用模糊综合评判理论确定广义基本信度赋值;接着采用DSmT融合规则对广义基本信度赋值合成,得到健康状态等级。对于多级的系统评估,可将融合后的结果作为新的广义基本信度赋值进行DSmT融合。实例验证表明,该状态评估模型能够准确有效地实现对系统的健康状态评估,而且能够克服高冲突证据的融合问题,具有良好的应用价值。     

QUALITATIVE REASONING BY COMPUTING WITH WORDS IN HERRERA-MARTÍNEZ’S LINGUISTIC MODEL

Most of modern systems for information retrieval, fusion and management have to deal with more and more qualitative information (by linguistic labels) besides information expressed quantita- tively (by numbers), since human reports are better and easier expressed in natural language than with numbers. In this paper, Herrera-Martínez’s 2-Tuple linguistic representation model is extended for reasoning with uncertain and qualitative information in Dezert-Smarandache Theory (DSmT) framework, in order to overcom...     

区间信度结构下的证据合成方法研究

在使用证据理论进行数据融合的过程中,有时精确的信度结构很难获得,此时需要对区间信度进行合成.本文分析了在DST和DSmT框架下的区间证据合成问题,对目前使用的方法进行了简要的回顾.通过对优化方法进行研究,提出了4种应用于区间信度组合的优化方法.CDI1~CDI4方法都可应用于DST和DSmT框架,对不精确,不确定以及冲突的信息进行合成,合成结果准确度逐步提高.文章最后给出了算例验证,并与其他区间信度合成方法的进行对比.     

DST与DSmT自适应融合算法

与DST相比，DSmT可以很好的解决证据矛盾时的证据组合问题，但是DSmT计算量过大，且在低冲突情况下融合结果次于DST。文章在DST和DSIllT的基础上提出一种将两种融合方法结合使用的融合算法——DST与DSmT自适应融合算法。以冲突率作为判决依据，在冲突率较低情况下采取DST融合算法．当冲突率高于一定阈值时采用DSmT融合算法，并给出了DST和DSmT之间转化的方法。在进行三维飞机序列图像的目标类型识别中．采用本文提出的自适应算法进行迭代运算，可以快速准确地进行目标识别。     

An approximate reasoning method in Dezert-Smarandache Theory

With the increment of focal elements number in discernment framework, the computation amount in Dezert-Smarandache Theory (DSmT) will exponentially go up. This has been the bottle- neck problem to block the wide application and development of DSmT. Aiming at this difficulty, in this paper, a kind of fast approximate reasoning method in hierarchical DSmT is proposed. Presently, this method is only fit for the case that there are only singletons with assignment in hyper-power set. These singletons in hy-per-power set are forced to group through bintree or tri-tree technologies. At the same time, the assignments of singletons in those different groups corresponding to each source are added up respectively, in order to realize the mapping from the refined hyper-power set to the coarsened one. And then, two sources with the coarsened hyper-power set are combined together according to classical DSm Combination rule (DSmC) and Proportional Conflict Redistribution rule No. 5 (PCR5). The fused results in coarsened framework will be saved as the connecting weights between father and children nodes. And then, all assignments of singletons in different groups will be normalized respectively. Tree depth is set, in order to decide the iterative times in hierarchical system. Finally, by comparing new method with old one from different views, the superiority of new one over old one is testified well.