针对K-means算法进行大跨屋盖结构表面风荷载分区中存在的分类数k值需凭经验事先给定以及所有初始聚类中心均需随机选取带来的分类情况数过多、从中寻找最优分类结果工作量大且效率低的问题,提出基于改进K-means算法的大跨屋盖结构表面...针对K-means算法进行大跨屋盖结构表面风荷载分区中存在的分类数k值需凭经验事先给定以及所有初始聚类中心均需随机选取带来的分类情况数过多、从中寻找最优分类结果工作量大且效率低的问题,提出基于改进K-means算法的大跨屋盖结构表面风荷载分区方法。首先,建立分类数k与其相应测点风荷载的误差平方和(Sum of the Squared Errors:SSE)关系曲线,引入手肘法基本思想,实现最优分类数kst值的精准识别;其次,在首个初始聚类中心随机选取基础上,引入轮盘法基本思想,完成对剩余初始聚类中心的高效选取;然后,根据类内紧凑、类间分散的原则,通过类内紧凑性判定指标S(k)和类间分散性判定指标D(k),构造并借助SD(k)值有效性检验,得到最优的风荷载分区结果;最后,以北京奥林匹克网球中心大跨悬挑屋盖结构为例,针对风洞试验所得风荷载测试结果,采用所提方法对其表面最不利风压系数进行分区计算,并与传统K-means算法进行对比,结果表明,所提方法能够高效实现大跨屋盖结构表面风压分区计算,具有较好的工程应用价值。展开更多
This study aims to reduce the statistical uncertainty of the correlation coefficient matrix in the mean-variance model of Markowitz. A filtering algorithm based on minimum spanning tree （MST） is proposed. Daily data...This study aims to reduce the statistical uncertainty of the correlation coefficient matrix in the mean-variance model of Markowitz. A filtering algorithm based on minimum spanning tree （MST） is proposed. Daily data of the 30 stocks of the Hang Seng Index （HSI） and Dow Jones Index （DJI） from 2004 to 2009 are selected as the base dataset. The proposed algorithm is compared with the Markowitz method in terms of risk, reliability, and effective size of the portfolio. Results show that （1） although the predicted risk of portfolio built with the MST is slightly higher than that of Markowitz, the realized risk of MST filtering algorithm is much smaller; and （2） the reliability and the effective size of filtering algorithm based on MST is apparently better than that of the Markowitz portfolio. Therefore, conclusion is that filtering algorithm based on MST improves the mean-variance model of Markowitz.展开更多
提出了基于局部均值分解(LMD)的同步电机参数辨识方法。采用LMD从短路电流中提取直流电流和基波电流,然后分别采用稳健回归最小二乘和Prony算法对直流电流和基波电流进行辨识,进而获得同步电机参数。以理想突然短路电流信号为例,通过仿...提出了基于局部均值分解(LMD)的同步电机参数辨识方法。采用LMD从短路电流中提取直流电流和基波电流,然后分别采用稳健回归最小二乘和Prony算法对直流电流和基波电流进行辨识,进而获得同步电机参数。以理想突然短路电流信号为例,通过仿真分析了滑动平均跨度与LMD循环次数和电流相对均方误差的关系,确定了滑动平均跨度。高信噪比(30 d B)时,由于LMD具有平滑滤波功能而无模态混叠现象发生。低信噪比(15 d B)时,提出了基于前置滑动平均LMD的短路电流分离方法,可有效获取直流电流和基波电流分量。较之经验模态分解(EMD),基于LMD的理想突然短路电流分解效果更好。仿真结果表明,与EMD方法相比,所提方法受噪声影响较小,参数辨识精度更高。展开更多
文摘针对K-means算法进行大跨屋盖结构表面风荷载分区中存在的分类数k值需凭经验事先给定以及所有初始聚类中心均需随机选取带来的分类情况数过多、从中寻找最优分类结果工作量大且效率低的问题,提出基于改进K-means算法的大跨屋盖结构表面风荷载分区方法。首先,建立分类数k与其相应测点风荷载的误差平方和(Sum of the Squared Errors:SSE)关系曲线,引入手肘法基本思想,实现最优分类数kst值的精准识别;其次,在首个初始聚类中心随机选取基础上,引入轮盘法基本思想,完成对剩余初始聚类中心的高效选取;然后,根据类内紧凑、类间分散的原则,通过类内紧凑性判定指标S(k)和类间分散性判定指标D(k),构造并借助SD(k)值有效性检验,得到最优的风荷载分区结果;最后,以北京奥林匹克网球中心大跨悬挑屋盖结构为例,针对风洞试验所得风荷载测试结果,采用所提方法对其表面最不利风压系数进行分区计算,并与传统K-means算法进行对比,结果表明,所提方法能够高效实现大跨屋盖结构表面风压分区计算,具有较好的工程应用价值。
基金supported by the funds project under the Ministry of Education of the PRC for young people who are devoted to the researches of humanities and social sciences under Grant No. 09YJC790025
文摘This study aims to reduce the statistical uncertainty of the correlation coefficient matrix in the mean-variance model of Markowitz. A filtering algorithm based on minimum spanning tree （MST） is proposed. Daily data of the 30 stocks of the Hang Seng Index （HSI） and Dow Jones Index （DJI） from 2004 to 2009 are selected as the base dataset. The proposed algorithm is compared with the Markowitz method in terms of risk, reliability, and effective size of the portfolio. Results show that （1） although the predicted risk of portfolio built with the MST is slightly higher than that of Markowitz, the realized risk of MST filtering algorithm is much smaller; and （2） the reliability and the effective size of filtering algorithm based on MST is apparently better than that of the Markowitz portfolio. Therefore, conclusion is that filtering algorithm based on MST improves the mean-variance model of Markowitz.
文摘提出了基于局部均值分解(LMD)的同步电机参数辨识方法。采用LMD从短路电流中提取直流电流和基波电流,然后分别采用稳健回归最小二乘和Prony算法对直流电流和基波电流进行辨识,进而获得同步电机参数。以理想突然短路电流信号为例,通过仿真分析了滑动平均跨度与LMD循环次数和电流相对均方误差的关系,确定了滑动平均跨度。高信噪比(30 d B)时,由于LMD具有平滑滤波功能而无模态混叠现象发生。低信噪比(15 d B)时,提出了基于前置滑动平均LMD的短路电流分离方法,可有效获取直流电流和基波电流分量。较之经验模态分解(EMD),基于LMD的理想突然短路电流分解效果更好。仿真结果表明,与EMD方法相比,所提方法受噪声影响较小,参数辨识精度更高。
