针对现有方法存在的区分度不高、运行时间代价过高等问题,提出一种云模型相似性度量方法。首先采用云模型的扩展模型三角云模型作为研究对象,将三角云模型看作对称三角模糊数,根据EW-型距离公式引入指数贴近度概念,并用其表征云模型的...针对现有方法存在的区分度不高、运行时间代价过高等问题,提出一种云模型相似性度量方法。首先采用云模型的扩展模型三角云模型作为研究对象,将三角云模型看作对称三角模糊数,根据EW-型距离公式引入指数贴近度概念,并用其表征云模型的距离相似度;然后通过云模型云滴的方差,计算出云模型的形状相似度;最后将云模型的距离与形状相似度综合起来,共同衡量两云模型的相似度。从仿真实验可以看出,该方法有较高的区分度;对Synthetic control chart data数据集进行的分类实验表明,该方法具有较好的分类精度及较小的运行时间代价。展开更多
针对现有的相似性度量方法中存在区分度不高、结果不稳定等问题,提出了一种基于EW-型贴近度的云模型相似性度量方法。该方法利用正态云模型的扩展模型三角云为研究对象,分别把三角云的期望曲线及最大边界曲线看作三角模糊数,通过计算三...针对现有的相似性度量方法中存在区分度不高、结果不稳定等问题,提出了一种基于EW-型贴近度的云模型相似性度量方法。该方法利用正态云模型的扩展模型三角云为研究对象,分别把三角云的期望曲线及最大边界曲线看作三角模糊数,通过计算三角模糊数的EW-型贴近度来度量云模型的相似性,充分考虑了期望曲线和最大边界曲线的特点,定义了一种综合的求两云模型相似度的计算方法。通过仿真实验可以看出,提出的EMTCM方法具有一定的区分度;在Synthetic Control Chart Dataset数据集上的分类对比实验表明,EMTCM方法的分类精度明显优于先前的LICM、ECM、MCM方法,验证了EMTCM方法有一定的可行性及有效性。展开更多
In order to study the triangulation for the point cloud data collected by three-dimensional laser radar,in accordance with the line-by-line characteristics of laser radar scanning,an improved Delaunay triangulation me...In order to study the triangulation for the point cloud data collected by three-dimensional laser radar,in accordance with the line-by-line characteristics of laser radar scanning,an improved Delaunay triangulation method is proposed to mesh the point cloud data as a triangulation irregular network.Based on the geometric topology location information among radar point cloud data,focusing on the position relationship between adjacent scanning line of the point data,a preliminary match network is obtained according to their geometric relationship.A reasonable triangulation network for the object surface is acquired after the use of local optimization on initial mesh by Delaunay rule.Meanwhile,a new judging rule is proposed to contrast the triangulation before and after the optimization on the network.The result shows that triangulation for point cloud with full use of its own characteristics can improve the speed of the algorithm obviously,and the rule for judging the triangulation can evaluate the quality of network.展开更多
文摘针对现有方法存在的区分度不高、运行时间代价过高等问题,提出一种云模型相似性度量方法。首先采用云模型的扩展模型三角云模型作为研究对象,将三角云模型看作对称三角模糊数,根据EW-型距离公式引入指数贴近度概念,并用其表征云模型的距离相似度;然后通过云模型云滴的方差,计算出云模型的形状相似度;最后将云模型的距离与形状相似度综合起来,共同衡量两云模型的相似度。从仿真实验可以看出,该方法有较高的区分度;对Synthetic control chart data数据集进行的分类实验表明,该方法具有较好的分类精度及较小的运行时间代价。
文摘针对现有的相似性度量方法中存在区分度不高、结果不稳定等问题,提出了一种基于EW-型贴近度的云模型相似性度量方法。该方法利用正态云模型的扩展模型三角云为研究对象,分别把三角云的期望曲线及最大边界曲线看作三角模糊数,通过计算三角模糊数的EW-型贴近度来度量云模型的相似性,充分考虑了期望曲线和最大边界曲线的特点,定义了一种综合的求两云模型相似度的计算方法。通过仿真实验可以看出,提出的EMTCM方法具有一定的区分度;在Synthetic Control Chart Dataset数据集上的分类对比实验表明,EMTCM方法的分类精度明显优于先前的LICM、ECM、MCM方法,验证了EMTCM方法有一定的可行性及有效性。
基金the National Natural Science Foundation of China (No. 50805094)the National Basic Research Program (973) of China (No. 2006CB705400)
文摘In order to study the triangulation for the point cloud data collected by three-dimensional laser radar,in accordance with the line-by-line characteristics of laser radar scanning,an improved Delaunay triangulation method is proposed to mesh the point cloud data as a triangulation irregular network.Based on the geometric topology location information among radar point cloud data,focusing on the position relationship between adjacent scanning line of the point data,a preliminary match network is obtained according to their geometric relationship.A reasonable triangulation network for the object surface is acquired after the use of local optimization on initial mesh by Delaunay rule.Meanwhile,a new judging rule is proposed to contrast the triangulation before and after the optimization on the network.The result shows that triangulation for point cloud with full use of its own characteristics can improve the speed of the algorithm obviously,and the rule for judging the triangulation can evaluate the quality of network.