In practical parameter estimation,we have always chosen either Least Squares Estimation(LSE) or Robust Estimation.Since the distribution of observations is unknown,to select a correct estimation method is very difficu...In practical parameter estimation,we have always chosen either Least Squares Estimation(LSE) or Robust Estimation.Since the distribution of observations is unknown,to select a correct estimation method is very difficult.It is well known that if observations include gross errors,the result of LSE will be badly containinated.On the other hand,if observations do not include any gross errors,the result of robust estimation is not as good as that of LSE.To solve this problem,Wang (1999) developed an estimation method called Information Spread Estimation (ISE) based on the information spread principle.The ISE is a very good method for estimating one parameter which is very robust.However, most of instances in surveying data processing are multi_parameters’ estimation,owing to the inherent restrictions of ISE,it can not be applied to the surveying data processing directly.To apply the good method to the field of surveying data processing widely,the author has done the research deeply.This paper applies ISE successfully to the adjustment of leveling network by using the specialties of leveling.展开更多
工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小...工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.展开更多
文摘In practical parameter estimation,we have always chosen either Least Squares Estimation(LSE) or Robust Estimation.Since the distribution of observations is unknown,to select a correct estimation method is very difficult.It is well known that if observations include gross errors,the result of LSE will be badly containinated.On the other hand,if observations do not include any gross errors,the result of robust estimation is not as good as that of LSE.To solve this problem,Wang (1999) developed an estimation method called Information Spread Estimation (ISE) based on the information spread principle.The ISE is a very good method for estimating one parameter which is very robust.However, most of instances in surveying data processing are multi_parameters’ estimation,owing to the inherent restrictions of ISE,it can not be applied to the surveying data processing directly.To apply the good method to the field of surveying data processing widely,the author has done the research deeply.This paper applies ISE successfully to the adjustment of leveling network by using the specialties of leveling.
文摘工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.
