| ID | 原文 | 译文 |
| 11904 | 其次,对现有部分相似度量方法在某些情况下无法表述的问题进行了分析,定义了满足直觉模糊相似性的直观约束条件,给出一种直觉模糊相似度量的公理化定义。 | Second, the existing part of the similarity measurement method in some cases, unable to describe the problem was analyzed, and defines the intuitive of intuitionistic fuzzy similarity constraint conditions, given a axiomatic definition of intuitionistic fuzzy similarity measures. |
| 11905 | 再次,揭示了直觉指数对证据的倾向性影响,提出了一种基于倾向性的直觉模糊相似度量方法。 | Again, to reveal the effect intuitionistic index on the orientation of the evidence, is put forward based on the orientation of intuitionistic fuzzy similarity measure. |
| 11906 | 最后,通过算例分析比较,验证该方法的正确性、合理性、有效性。 | Finally, through case analysis and comparison, verify the correctness of this method, the rationality and validity. |
| 11907 | 针对Markov链在预测概率发生跳变时无法有效地衡量样本归属程度的问题,提出一种云化Markov链的状态预测方法,通过云模型描述和处理样本的不确定性。 | Change for Markov chains in the prediction probability when unable to effectively measure samples belonging degree of problem, put forward a kind of cloud state of the Markov chain prediction method, and through the cloud model is described and the uncertainty of processing samples. |
| 11908 | 该方法将划分的状态区间视作一种概念,利用云模型对其进行云化表示,据此计算样本对各概念的确定度,得到概念之间的概率转移矩阵,从而实现带有随机特性的状态预测。 | This method will be divided into the state of the interval as a concept, using cloud model on the cloud said, on the basis of calculating sample to the uncertainty of the concept of the transfer matrix of probability between the concept, so as to realize state prediction with random characteristics. |
| 11909 | 概念转移概率作为关键随机变量,对其进行了核密度估计。 | Concept of transfer probability and random variables, as a key to the kernel density estimation. |
| 11910 | 最后以多次随机实验的概率和提取代表性转移概率分别给出了仿真实验结果。 | The last representative is extracted by the probability of random experiment many times and transition probability simulation results are given. |
| 11911 | 表明该不确定性描述的预测方法在解决Markov链预测概率跳变现象的同时,可通过确定度的分配有效地表述样本的归属程度,具有较好的实用性。 | Respectively show that the uncertainty description of prediction method to solve Markov chain prediction probability jump phenomenon at the same time, can effectively describe the distribution of the sample by determining degrees of belonging degree, has good practicability. |
| 11912 | 为了弥补阵列天线导向矢量失配和相位测量噪声对测向性能的影响,提出一种基于稳健capon波束形成技术(robust capon beamforming,RCB)和锁相环的矢量最优估计与跟踪鉴别测向方法。 | To make up for the antenna array steering vector mismatch and phase measurement noise effect on the performance of direction finding, based on a robust capon beamforming (robust capon beamforming, RCB) and phase locked loop vector, the optimal estimation and tracking identification method of direction finding. |
| 11913 | 首先基于RCB与锁相环原理,对目标来波信号导向矢量进行最优估计与跟踪测量; | First of all, on the basis of the principle of RCB with phase-locked loop optimal of target to wave signal steering vector estimation and tracking measurement; |