In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis ...In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.展开更多
区别于经典的基于Parzen窗口法的概率密度函数估计器构建策略,提出了基于近邻误差度量函数的启发式核密度估计器(Heuristic kernel density estimator,HKDE),用以提升对模相近数据概率密度函数拟合的准确性。首次从数据不确定性和模型...区别于经典的基于Parzen窗口法的概率密度函数估计器构建策略,提出了基于近邻误差度量函数的启发式核密度估计器(Heuristic kernel density estimator,HKDE),用以提升对模相近数据概率密度函数拟合的准确性。首次从数据不确定性和模型不确定性的角度分析了传统核密度估计器解决模相近数据概率密度函数估计问题时的缺陷:利用概率密度值对于直方图箱宽参数的收敛性确定观测数据的启发式概率密度值,降低数据概率密度值计算的不确定性;基于启发式概率密度值构建用于确定核密度估计器最优带宽的目标函数,降低最优带宽优化过程中的不确定性。在18个模相近数据集上对新估计器HKDE的可行性、合理性和有效性进行了系统性的验证。实验结果表明,与7种具有代表性的概率密度函数估计器相比,HKDE能够获得更加优异的概率分布近似表现,具有比其他估计器更低的估计误差,能够确定出更接近真实值的概率密度函数估计值。展开更多
光伏发电在能源领域中具有重要地位。为了准确量化光伏发电功率的不确定性和波动范围,并提高区间预测的综合性能,提出了一种基于特征挖掘与改进TCN-BiGRU的光伏功率区间概率预测方法。首先,利用最大信息系数和符号传递熵因果分析,对气...光伏发电在能源领域中具有重要地位。为了准确量化光伏发电功率的不确定性和波动范围,并提高区间预测的综合性能,提出了一种基于特征挖掘与改进TCN-BiGRU的光伏功率区间概率预测方法。首先,利用最大信息系数和符号传递熵因果分析,对气象特征进行筛选,剔除冗余信息,并构造全球水平辐射趋势特征、季节性特征和天气聚类特征以提供更多有效信息。随后,结合时间模式注意力机制和分位数回归方法对TCN-BiGRU模型进行改进,构建组合模型进行区间预测。最后,采用散度度量半极差优化经验带宽选择的核密度估计(kernel density estimation,KDE)方法生成概率预测结果。通过真实光伏电站数据进行分析,验证了所提方法在光伏功率区间概率预测中具有较高的可靠性和适用性。展开更多
基金Project(61101185) supported by the National Natural Science Foundation of ChinaProject(2011AA1221) supported by the National High Technology Research and Development Program of China
文摘In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.
文摘区别于经典的基于Parzen窗口法的概率密度函数估计器构建策略,提出了基于近邻误差度量函数的启发式核密度估计器(Heuristic kernel density estimator,HKDE),用以提升对模相近数据概率密度函数拟合的准确性。首次从数据不确定性和模型不确定性的角度分析了传统核密度估计器解决模相近数据概率密度函数估计问题时的缺陷:利用概率密度值对于直方图箱宽参数的收敛性确定观测数据的启发式概率密度值,降低数据概率密度值计算的不确定性;基于启发式概率密度值构建用于确定核密度估计器最优带宽的目标函数,降低最优带宽优化过程中的不确定性。在18个模相近数据集上对新估计器HKDE的可行性、合理性和有效性进行了系统性的验证。实验结果表明,与7种具有代表性的概率密度函数估计器相比,HKDE能够获得更加优异的概率分布近似表现,具有比其他估计器更低的估计误差,能够确定出更接近真实值的概率密度函数估计值。
文摘光伏发电在能源领域中具有重要地位。为了准确量化光伏发电功率的不确定性和波动范围,并提高区间预测的综合性能,提出了一种基于特征挖掘与改进TCN-BiGRU的光伏功率区间概率预测方法。首先,利用最大信息系数和符号传递熵因果分析,对气象特征进行筛选,剔除冗余信息,并构造全球水平辐射趋势特征、季节性特征和天气聚类特征以提供更多有效信息。随后,结合时间模式注意力机制和分位数回归方法对TCN-BiGRU模型进行改进,构建组合模型进行区间预测。最后,采用散度度量半极差优化经验带宽选择的核密度估计(kernel density estimation,KDE)方法生成概率预测结果。通过真实光伏电站数据进行分析,验证了所提方法在光伏功率区间概率预测中具有较高的可靠性和适用性。
