CAJ | 학술논문

文章通过多层采样方式，将样本空间划分为多个部分，集中采样点到使概率密度函数值大的地方，大大减小了采样误差；在重采样阶段嵌入KHM聚类算法，通过将空间特征与权重分布近似的粒子进行聚类，降低总的样本数，提高了计算效率。样本经聚类处理后，在保持粒子状态后验分布的几何特征的同时，状态空间中的粒子数明显降低，计算效率显著提高。
문장통과다층채양방식，장양본공간화분위다개부분，집중채양점도사개솔밀도함수치대적지방，대대감소료채양오차；재중채양계단감입KHM취류산법，통과장공간특정여권중분포근사적입자진행취류，강저총적양본수，제고료계산효솔。양본경취류처리후，재보지입자상태후험분포적궤하특정적동시，상태공간중적입자수명현강저，계산효솔현저제고。
In this paper ,by using the stratified sampling method ,the sample space is divided into mul‐tiple parts ,grouping sampling points to the part of high probability density function value ,so the sampling error is greatly reduced .In the resampling phase ,the K HM clustering algorithm is embed‐ded ,the particles with approximate spatial characteristics and weight distribution are clustered ,thus reducing the total number of samples and improving the computing efficiency .After the clustering , the geometrical characteristics of particles state posterior distribution is maintained ,w hile the number of particles in the state space is significantly reduced and the computing efficiency is improved .