一种结合证据理论和Vague集信息融合方法

针对强冲突下证据理论结果与直觉相悖及Vague集数值获取主观性强等问题,提出一种结合证据理论和Vague集的新方法.该方法将获取证据理论的信度函数转化为Vague集区间值形式,通过求解与理想点的距离进行决策.在进行信度函数到Vague集区间值的转化时,提出一种基于聚焦度递减顺次分配的思想,经分析得出该思想与人的主观思想是一致的,最后举例阐明整个算法过程.     

A new non-specificity measure in evidence theory based on belief intervals

In the theory of belief functions, the measure of uncertainty is an important concept, which is used for representing some types of uncertainty incorporated in bodies of evidence such as the discord and the non-specificity. For the non-specificity part, some traditional measures use for reference the Hartley measure in classical set theory;other traditional measures use the simple and heuristic function for joint use of mass assignments and the cardinality of focal elements. In this paper, a new non-specificity measure is proposed using lengths of belief intervals, which represent the degree of imprecision. Therefore, it has more intuitive physical meaning. It can be proved that our new measure can be rewritten in a general form for the non-specificity. Our new measure is also proved to be a strict non-specificity measure with some desired properties. Numerical examples, simulations, the related analyses and proofs are provided to show the characteristics and good properties of the new non-specificity definition. An example of an application of the new non-specificity measure is also presented.     

基于分层置信规则库的惯导系统性能评估方法

为解决惯导系统（INS）性能评估所面临的高价值样本缺失、评估指标多、系统复杂等问题,提出一种基于分层置信规则库（Hierarchical BRB）的惯导系统性能评估方法。将专家知识与监测数据进行有效融合,提高了惯导系统的性能评估精度。首先,针对惯导系统结构构建分层BRB模型,同时将系统内部器件产生组合误差考虑在模型中。其次,为降低专家知识不确定性对初始模型评估精度的影响,采用基于投影算子的协方差矩阵自适应优化策略（P-CMA-ES）构建优化模型,通过监测数据对模型参数进行微调。最后,以某型捷联惯导系统的性能评估为例,验证了所提方法的有效性。     

De-combination of belief function based on optimization

In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments(BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence d...     

Novel moderate transformation of fuzzy membership function into basic belief assignment

In information fusion, the uncertain information from different sources might be modeled with different theoretical frameworks. When one needs to fuse the uncertain information represented by different uncertainty theories, constructing the transformation between different frameworks is crucial. Various transformations of a Fuzzy Membership Function (FMF) into a Basic Belief Assignment (BBA) have been proposed, where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements. However, these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty. To avoid extreme attitudinal bias, a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred. In this paper, two moderate transformations of an FMF into a trade-off BBA are proposed. One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism, where the weighting factor can be user-specified or determined with some prior information. The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.     

A new interpretable fault diagnosis method based on belief rule base and probability table

It is vital to establish an interpretable fault diagnosis model for critical equipment. Belief Rule Base (BRB) is an interpretable expert system gradually applied in fault diagnosis. However, the expert knowledge cannot be utilized to establish the initial BRB accurately if there are multiple referential grades in different fault features. In addition, the interpretability of BRB-based fault diagnosis is destroyed in the optimization process, which reflects in two aspects: deviation from the initial expert judgment and over-optimization of parameters. To solve these problems, a new interpretable fault diagnosis model based on BRB and probability table, called the BRB-P, is proposed in this paper. Compared with the traditional BRB, the BRB-P constructed by the probability table is more accurate. Then, the interpretability constraints, i.e., the credibility of expert knowledge, the penalty factor and the rule-activation factor, are inserted into the projection covariance matrix adaption evolution strategy to maintain the interpretability of BRB-P. A case study of the aerospace relay is conducted to verify the effectiveness of the proposed method.     

Synthetic damage effect assessment through evidential reasoning approach and neural fuzzy inference: Application in ship target

The damage effect assessment of anti-ship missiles combines system science and weapon science, which can provide reference for the assessment of battlefield damage situation. In order to solve the difficulty of heterogeneous data aggregation and the difficulty in constructing the mapping between factors and damage effect, this paper analyzes the specific damage process of the anti-ship missile to the ship, and proposes a synthetic Evidential Reasoning (ER) – Adaptive Neural Fuzzy Inference System (ANFIS) to assess the damage effect. To solve the problem of fuzziness and uncertainty of criteria in the assessment process, the belief structure model is used to transform qualitative and quantitative information into a unified mathematical structure, and ER algorithm is used to fuse the information of lower-level criteria. In order to solve the problem of fuzziness and uncertainty of the information contained in the first-level variables, and the strong non-linear characteristics of the mapping between first-level variables and damage effect, the ANFIS with self-adaptation and self-learning is constructed. The map between the three first-level variables and damage effect is established, and the interaction process of the various factors in the damage effect assessment are clear. Sensitivity analysis shows that assessment model has good stability. The result analysis and comparative analysis show that the process proposed in this paper can effectively assess the damage effect of anti-ship missiles, and all criteria data are objective and comparable.     

A novel combination belief rule base model for mechanical equipment fault diagnosis

Due to the excellent performance in complex systems modeling under small samples and uncertainty, Belief Rule Base(BRB) expert system has been widely applied in fault diagnosis. However, the fault diagnosis process for complex mechanical equipment normally needs multiple attributes, which can lead to the rule number explosion problem in BRB, and limit the efficiency and accuracy. To solve this problem, a novel Combination Belief Rule Base(C-BRB) model based on Directed Acyclic Graph(DAG) structu...