ID 原文 译文
18525 然后根据符号协方差矩阵的映射等效性和特征空间不变性，将符号协方差矩阵应用到最大似然(SCM-ML)测高算法中，实现了稳健的米波雷达低角测高。 Then the application of SCM to the MaximumLikelihood method(SCM-ML) is presented on the basis of the affine equivalence and preservation of theeigen structure for robust low elevation estimation and height finding of VHF radar.
18526 该算法有效抑制了多径信号中散射分量和波束打地形成的强杂波的非高斯性，提高了米波雷达低角测高的稳健性。 The proposed method effectively solves the non-Gaussian property of the diffuse multipath component and improves the robustness of low elevation estimation.
18527 仿真结果和实测数据验证了算法的稳健性与有效性。 Simulation result and real data demonstrate the robustness and validation of theSCM-ML method.
18528 为了更快速且精确地诊断出大规模多处理器系统中的故障单元，该文首次将改进的烟花算法和反向传播(BP)神经网络相结合，提出一种新的系统级故障诊断算法—烟花-反向传播神经网络故障诊断算法(FWA-BPFD)。 In order to diagnose fault units in the large-scale multiprocessor systems more quickly and accurately,a system-level fault diagnosis algorithm—FireWorks Algorithm-Back Propagation Fault Diagnosis (FWA-BPFD) based on fireworks algorithm and Back Propagation(BP) neural network is proposed.
18529 首先，在烟花算法中引入双种群策略、协作算子以及最优算子，设计新的适应度函数，优化变异算子、映射规则和选择策略。 Firstly, two population strategy, cooperative operator and optimal operator are introduced into fireworks algorithm. A new fitness function is designed, and the mutation operator, mapping rule and selection strategy are optimized.
18530 然后，利用烟花算法全局搜索能力和局部搜索能力的自调节机制，优化BP神经网络中的权值和阈值的寻优过程。 Then, the optimization process of weight and threshold value in BP neural network is optimized by the self-regulating mechanism of global and local searching ability of fireworks algorithm.
18531 仿真实验结果表明，该文算法相较于其他算法不仅有效地降低了迭代次数和训练时间，而且还进一步提高了诊断精度。 Simulation results show that compared with other algorithms, this algorithm not only reduces the number of iterations and training time, but also improves the accuracy of diagnosis.
18532 类属属性学习避免相同属性预测全部标记，是一种提取各标记独有属性进行分类的一种框架，在多标记学习中得到广泛的应用。 The label-specific features learning avoids the same features prediction for all class labels, it is a kind of framework for extracting the specific features of each label for classification, so it is widely used in multi-label learning.
18533 而针对标记维度较大、标记分布密度不平衡等问题，已有的基于类属属性的多标记学习算法普遍时间消耗大、分类精度低。 For the problems of large label dimension and unbalanced label distribution density, the existing multi-label learning algorithm based on label-specific features has larger time consumption and lower classification accuracy.
18534 为提高多标记分类性能，该文提出一种基于标记密度分类间隔面的组类属属性学习(GLSFL-LDCM)方法。 In order to improve the performance of classification, a Group-Label-Specific FeaturesLearning method based on Label-Density Classification Margin (GLSFL-LDCM) is proposed.