ID 原文 译文
16925 然后通过建立两矩阵中元素及元素位置间的若干映射，从而实现了一种从随机整数集合中生成二分聚类初始中心对的线性复杂度算法。 Then, by establishing several mappingsbetween the elements and their positions in the two matrices, a linear complexity algorithm is proposed togenerate initial center pairs from the set of random integers.
16926 理论分析与实验结果均表明，该方法的时间效率及效率稳定性均明显优于常用的随机采样方法，特别适用于高维大数据聚类场景。 Both theoretical analysis and experimental results show that the time efficiency and efficiency stability of this method are significantly better than the current methods of random sampling, so it is particularly suitable for these scenarios of high-dimensional big data clustering.
16927 针对复杂环境下常规直方图信号分选算法对于参差信号分选能力不佳的问题，该文提出一种基于脉冲间隔与单个脉冲关联的直方图算法。 For the conventional histogram signal deinterleaving algorithm’s drawback of stagger signal, ahistogram algorithm based on corresponding of pulse interval and single pulse is proposed.
16928 该算法根据脉冲间隔与单个脉冲的对应关系建立了脉冲间隔分布矩阵(PIDM)，然后通过对PIDM行列的累加计算，得到一种新的直方图， This algorithmutilizes the corresponding of pulse pair interval and single pulse to get a matrix named Pulse IntervalDistribution Matrix (PIDM), and a novel histogram is obtained via cumulating row of the matrix.
16929 该直方图可避免传统脉冲重复间隔(PRI)变换算法在分选参差信号时对于参差信号帧周期过多抑制的缺陷，且能够通过PIDM对辐射源脉冲串进行序列提取，进而得到参差子序列的周期值。 This histogram can avoid the suppressing of frame period of staggered pulse train caused by Pulse RepetitionIntervals (PRI) transform algorithm when staggering signal deinterleaving, and can extract the subsequence of pulse train through PIDM.
16930 仿真分析结果表明，在不增加计算复杂度的情况下，该算法对存在多部参差辐射源和固定重频辐射源的混合场景仍可保持良好的分选效果。 Simulation results show the algorithm has excellent performance on environmentincluding multi staggered pulse trains with multi-fixed pulse trains under the circumstance of without addingthe complexity of calculating.
16931 为提高移动机器人在同步定位和地图构建(SLAM)中的定位精度，该文提出一种基于自组织可增长映射(GSOM)的仿生定位算法。 In order to improve the positioning accuracy of mobile robots in Simultaneous Localization AndMapping (SLAM), a bionic localization algorithm based on Growing Self-Organizing Map(GSOM) neuralnetwork is proposed.
16932 该方法将位置细胞的激活特性和神经网络输出层神经元建立响应连接，通过GSOM神经网络构建空间的拓扑地图，利用感知距离信息实现位置细胞的激活响应从而估计机器人位置，以此还原机器人的运行路径。 The method connects the activation characteristics of the place cells with the neuralnetwork output layer neurons to establish a response, and constructs a spatial topology map through theGSOM neural network, and uses the perceived distance information to realize the activation response of theplace cells to estimate the position of the robot. The running path of the robot is restored in this way.
16933 实验结果表明细胞间隔R对定位精度有较大影响，选取合适的细胞间隔能有效地减少神经网络的学习时间，提高定位精度， The experimental results show that the cell spacing R has a great influence on the positioning accuracy. Choosingthe appropriate cell spacing can effectively reduce the learning time of the neural network and improve thepositioning accuracy.
16934 该文算法平均误差在0.153 m以内，定位精度达到90.243%，均优于原有算法。 The average error of the algorithm is less than 0.153 m, and the positioning accuracy is90.243%, which is better than the original algorithm.