传统的误差配准算法假设系统偏差恒定或缓慢变化,当系统误差发生突变或快速变化时,这一假设不再成立。针对这一问题,研究了时变条件下的误差配准算法,引入渐消因子,对常规的基于地心地固坐标系的广义最小二乘算法(generalized least squ...传统的误差配准算法假设系统偏差恒定或缓慢变化,当系统误差发生突变或快速变化时,这一假设不再成立。针对这一问题,研究了时变条件下的误差配准算法,引入渐消因子,对常规的基于地心地固坐标系的广义最小二乘算法(generalized least squares algorithm based on the earth-centered earth-fixed coordinate system,ECEF-GLS)进行了修正,弱化历史量测对配准的影响,并对渐消因子的选取问题进行了研究,给出了合理的设计方法。算法验证表明,基于渐消因子的ECEF-GLS估计算法能够对时变的系统偏差进行有效估计,精度满足配准要求。展开更多
Many applications require the solution of large nonsymmetric linear systems with multiple right hand sides. Instead of applying an iterative method to each of these systems individually, it is often more efficient to...Many applications require the solution of large nonsymmetric linear systems with multiple right hand sides. Instead of applying an iterative method to each of these systems individually, it is often more efficient to use a block version of the method that generates iterates for all the systems simultaneously. In this paper, we propose a block version of generalized minimum backward (GMBACK) for solving large multiple nonsymmetric linear systems. The new method employs the block Arnoldi process to construct a basis for the Krylov subspace K m(A, R 0) and seeks X m∈X 0+K m(A, R 0) to minimize the norm of the perturbation to the data given in A.展开更多
目的比较不同算法对桂枝茯苓胶囊内容物吸湿性预测模型性能的影响,确定最优建模算法。方法以54个物理性质参数为输入,胶囊内容物吸湿性为输出,对比偏最小二乘算法(partial least squares,PLS)、决策树算法(classification and regressio...目的比较不同算法对桂枝茯苓胶囊内容物吸湿性预测模型性能的影响,确定最优建模算法。方法以54个物理性质参数为输入,胶囊内容物吸湿性为输出,对比偏最小二乘算法(partial least squares,PLS)、决策树算法(classification and regression tree,CART)、多元自适应回归样条算法(multivariate adaptive regression splines,MARS)和广义路径追踪算法(generalized path seeker,GPS)对建立吸湿性预测模型性能的影响。结果MARS算法建立的预测模型性能最佳,预测能力最强,模型的校正集决定系数(R2c)为0.843,预测集决定系数(R2p)为0.808,校正集均方根误差(root mean square error of calibration,RMSEC)为0.391,预测集均方根误差(root mean square error of prediction,RMSEP)为0.472,平均相对预测误差为2.69%,小于5%。结论MARS算法建立的吸湿性预测模型更适合桂枝茯苓胶囊的生产应用,该算法可嵌入在线控制系统,为生产过程的质量控制智能化提供技术支持。展开更多
文摘传统的误差配准算法假设系统偏差恒定或缓慢变化,当系统误差发生突变或快速变化时,这一假设不再成立。针对这一问题,研究了时变条件下的误差配准算法,引入渐消因子,对常规的基于地心地固坐标系的广义最小二乘算法(generalized least squares algorithm based on the earth-centered earth-fixed coordinate system,ECEF-GLS)进行了修正,弱化历史量测对配准的影响,并对渐消因子的选取问题进行了研究,给出了合理的设计方法。算法验证表明,基于渐消因子的ECEF-GLS估计算法能够对时变的系统偏差进行有效估计,精度满足配准要求。
文摘Many applications require the solution of large nonsymmetric linear systems with multiple right hand sides. Instead of applying an iterative method to each of these systems individually, it is often more efficient to use a block version of the method that generates iterates for all the systems simultaneously. In this paper, we propose a block version of generalized minimum backward (GMBACK) for solving large multiple nonsymmetric linear systems. The new method employs the block Arnoldi process to construct a basis for the Krylov subspace K m(A, R 0) and seeks X m∈X 0+K m(A, R 0) to minimize the norm of the perturbation to the data given in A.
文摘目的比较不同算法对桂枝茯苓胶囊内容物吸湿性预测模型性能的影响,确定最优建模算法。方法以54个物理性质参数为输入,胶囊内容物吸湿性为输出,对比偏最小二乘算法(partial least squares,PLS)、决策树算法(classification and regression tree,CART)、多元自适应回归样条算法(multivariate adaptive regression splines,MARS)和广义路径追踪算法(generalized path seeker,GPS)对建立吸湿性预测模型性能的影响。结果MARS算法建立的预测模型性能最佳,预测能力最强,模型的校正集决定系数(R2c)为0.843,预测集决定系数(R2p)为0.808,校正集均方根误差(root mean square error of calibration,RMSEC)为0.391,预测集均方根误差(root mean square error of prediction,RMSEP)为0.472,平均相对预测误差为2.69%,小于5%。结论MARS算法建立的吸湿性预测模型更适合桂枝茯苓胶囊的生产应用,该算法可嵌入在线控制系统,为生产过程的质量控制智能化提供技术支持。
