风电功率预测(wind power prediction,WPP)技术是电力系统调度与安全运行的关键性因素,为了更好地提升风电功率预测技术的精度,在集成学习的基础上提出了一种多重集成的集群短期WPP方法。所提方法包含4步:第1步,利用变分模式分解、经验...风电功率预测(wind power prediction,WPP)技术是电力系统调度与安全运行的关键性因素,为了更好地提升风电功率预测技术的精度,在集成学习的基础上提出了一种多重集成的集群短期WPP方法。所提方法包含4步:第1步,利用变分模式分解、经验模态分解和小波变换将原始风电序列分解为多个子序列;第2步,根据子序列构造多个堆叠去噪自动编码器(stacked denoising autoencoders,SDAE)进行深度学习;第3步,将第2步的结果随机划分成几个集合,利用支持向量机(support vector machine,SVM)对每个集合进行集成;第4步,将第3步的集成的结果再随机划分成几个集合,利用SVM对每个集合进行集成,重复以上步骤直至得到最终的集成预测结果。结果表明,多重集成学习得到前96 h预测结果的平均归一化均方根误差相比单次集成减少了0.0101,百分比为9.01%;相比SDAE减少了0.0151,百分比为13.54%;相比SVM减少了0.0175,百分比为14.66%。论文研究可为基于深度学习和集成学习的风电集群短期功率预测提供参考。展开更多
针对装甲车辆运动状态复杂性、战场态势不确定性、战术迷惑和欺骗性导致装甲车辆集群运动轨迹难以准确预测的问题,提出一种基于密度的空间聚类应用(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)和长短时记忆(L...针对装甲车辆运动状态复杂性、战场态势不确定性、战术迷惑和欺骗性导致装甲车辆集群运动轨迹难以准确预测的问题,提出一种基于密度的空间聚类应用(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)和长短时记忆(Long Short Term Memory,LSTM)神经网络的装甲车辆集群轨迹预测方法。根据装甲车辆的斜坡上行驶、转向和车-车交互行驶状态,建立运动学模型。选取机动特征、环境特征和车-车交互特征等轨迹特征信息,基于双层LSTM网络预测单个装甲车辆的轨迹。基于DBSCAN算法将多条单装预测轨迹进行分段、相似度计算和聚类,获得集群代表轨迹作为装甲车辆集群的预测轨迹。仿真结果表明,所提方法能够有效预测装甲车辆集群轨迹,实现料敌于先、谋敌于前。展开更多
This paper concerns the dimension reduction in regression for large data set. The authors introduce a new method based on the sliced inverse regression approach, cMled cluster-based regularized sliced inverse regressi...This paper concerns the dimension reduction in regression for large data set. The authors introduce a new method based on the sliced inverse regression approach, cMled cluster-based regularized sliced inverse regression. The proposed method not only keeps the merit of considering both response and predictors＇ information, but also enhances the capability of handling highly correlated variables. It is justified under certain linearity conditions. An empirical application on a macroeconomic data set shows that the proposed method has outperformed the dynamic factor model and other shrinkage methods.展开更多
Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete par...Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.展开更多
文摘针对装甲车辆运动状态复杂性、战场态势不确定性、战术迷惑和欺骗性导致装甲车辆集群运动轨迹难以准确预测的问题,提出一种基于密度的空间聚类应用(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)和长短时记忆(Long Short Term Memory,LSTM)神经网络的装甲车辆集群轨迹预测方法。根据装甲车辆的斜坡上行驶、转向和车-车交互行驶状态,建立运动学模型。选取机动特征、环境特征和车-车交互特征等轨迹特征信息,基于双层LSTM网络预测单个装甲车辆的轨迹。基于DBSCAN算法将多条单装预测轨迹进行分段、相似度计算和聚类,获得集群代表轨迹作为装甲车辆集群的预测轨迹。仿真结果表明,所提方法能够有效预测装甲车辆集群轨迹,实现料敌于先、谋敌于前。
基金supported by the National Science Foundation of China under Grant No.71101030the Program for Innovative Research Team in UIBE under Grant No.CXTD4-01
文摘This paper concerns the dimension reduction in regression for large data set. The authors introduce a new method based on the sliced inverse regression approach, cMled cluster-based regularized sliced inverse regression. The proposed method not only keeps the merit of considering both response and predictors＇ information, but also enhances the capability of handling highly correlated variables. It is justified under certain linearity conditions. An empirical application on a macroeconomic data set shows that the proposed method has outperformed the dynamic factor model and other shrinkage methods.
基金partly supported by the Natural Science Foundation of China under Grant Nos.71101100 and 70731160635New Teachers’Fund for Doctor Stations,Ministry of Education under Grant No.20110181120047+5 种基金Excellent Youth Fund of Sichuan University under Grant No.2013SCU04A08China Postdoctoral Science Foundation under Grant Nos.2011M500418,2012T50148 and 2013M530753Frontier and Cross-innovation Foundation of Sichuan University under Grant No.skqy201352Soft Science Foundation of Sichuan Province under Grant No.2013ZR0016Humanities and Social Sciences Youth Foundation of the Ministry of Education of China under Grant No.11YJC870028Selfdetermined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE under Grant No.CCNU13F030
文摘Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.
