大气加权平均温度T_(m)是GNSS探测大气可降水量PWV(Precipitable Water Vapor)的关键参数.目前,加权平均温度模型主要包括线性模型和非线性模型.本文基于2011—2015年期间的编号54511北京探空测站的有效探测资料,建立T_(m)与T_(s)的线...大气加权平均温度T_(m)是GNSS探测大气可降水量PWV(Precipitable Water Vapor)的关键参数.目前,加权平均温度模型主要包括线性模型和非线性模型.本文基于2011—2015年期间的编号54511北京探空测站的有效探测资料,建立T_(m)与T_(s)的线性和非线性(一阶傅里叶函数、一元二次函数)关系;利用2016年探空站实测资料对所建模型及常用模型进行对比分析,从RMSE、Bias及波动范围评价参数发现T_(m_G)模型精度高于常用模型,而再分析资料ERA-Interim建立的加权平温度T_(m)_ERA模型和新非线性T_(m)模型精度相差甚小,且误差概率分布趋近于正态分布;因此,新建模型能有效避免了通用Bevis全球模型在特定区域导致的区域性精度偏差问题,尤其在探空站缺乏的区域,可以采用ERA-Interim产品建立T_(m)模型.通过对不同T_(m)模型获取IGS站BJFS的PWV结果与相应时间54511探空站的实测PWV数据进行检验,结果表明不同T_(m)模型引起的PWV的偏差Bias范围在[-5,5]mm,均方根误差RMSE的差异甚小,Bias概率趋于正态分布,稳定性较强,尤其T_(m)_ERA、非线性加权平均温度T_(m_F)、T_(m_P)模型引起的PWV的Bias正态分布更强.展开更多
Portfolio management is a typical decision making problem under incomplete,sometimes unknown, information. This paper considers the portfolio selection problemsunder a general setting of uncertain states without proba...Portfolio management is a typical decision making problem under incomplete,sometimes unknown, information. This paper considers the portfolio selection problemsunder a general setting of uncertain states without probability. The investor's preferenceis based on his optimum degree about the nature, and his attitude can be described by anOrdered Weighted Averaging Aggregation function. We construct the OWA portfolio selection model, which is a nonlinear programming problem. The problem can be equivalentlytransformed into a mixed integer linear programming. A numerical example is given andthe solutions imply that the investor's strategies depend not only on his optimum degreebut also on his preference weight vector. The general game-theoretical portfolio selectionmethod, max-min method and competitive ratio method are all the special settings of thismodel.展开更多
文摘大气加权平均温度T_(m)是GNSS探测大气可降水量PWV(Precipitable Water Vapor)的关键参数.目前,加权平均温度模型主要包括线性模型和非线性模型.本文基于2011—2015年期间的编号54511北京探空测站的有效探测资料,建立T_(m)与T_(s)的线性和非线性(一阶傅里叶函数、一元二次函数)关系;利用2016年探空站实测资料对所建模型及常用模型进行对比分析,从RMSE、Bias及波动范围评价参数发现T_(m_G)模型精度高于常用模型,而再分析资料ERA-Interim建立的加权平温度T_(m)_ERA模型和新非线性T_(m)模型精度相差甚小,且误差概率分布趋近于正态分布;因此,新建模型能有效避免了通用Bevis全球模型在特定区域导致的区域性精度偏差问题,尤其在探空站缺乏的区域,可以采用ERA-Interim产品建立T_(m)模型.通过对不同T_(m)模型获取IGS站BJFS的PWV结果与相应时间54511探空站的实测PWV数据进行检验,结果表明不同T_(m)模型引起的PWV的偏差Bias范围在[-5,5]mm,均方根误差RMSE的差异甚小,Bias概率趋于正态分布,稳定性较强,尤其T_(m)_ERA、非线性加权平均温度T_(m_F)、T_(m_P)模型引起的PWV的Bias正态分布更强.
文摘Portfolio management is a typical decision making problem under incomplete,sometimes unknown, information. This paper considers the portfolio selection problemsunder a general setting of uncertain states without probability. The investor's preferenceis based on his optimum degree about the nature, and his attitude can be described by anOrdered Weighted Averaging Aggregation function. We construct the OWA portfolio selection model, which is a nonlinear programming problem. The problem can be equivalentlytransformed into a mixed integer linear programming. A numerical example is given andthe solutions imply that the investor's strategies depend not only on his optimum degreebut also on his preference weight vector. The general game-theoretical portfolio selectionmethod, max-min method and competitive ratio method are all the special settings of thismodel.
