Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confronta...Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.展开更多
创新是新时代医学教育改革发展的生命线。本文立足于“健康中国”建设大背景,介绍四川大学华西医院全科医学科实践经验和成果,结合医疗服务、管理、教育、学术能力四方面核心岗位胜任力的构建,以“全科-专科”和“医院-社区”协同整合...创新是新时代医学教育改革发展的生命线。本文立足于“健康中国”建设大背景,介绍四川大学华西医院全科医学科实践经验和成果,结合医疗服务、管理、教育、学术能力四方面核心岗位胜任力的构建,以“全科-专科”和“医院-社区”协同整合规划轮转计划,训练家庭医生团队协作精神,融入亚专长全科医生(general practitioners with special interests,GPwSI)培养,并采用形成性评价和终结性评价相结合的多样化评估,创新与“健康中国”行动相适应的高效、可复制、可推广的全科医学专科医师规范化培训(以下简称“全科专培”)实施方案,以期为新形势下全科专培制度教学改革提供参考。展开更多
基金the support of the Fundamental Research Funds for the Air Force Engineering University under Grant No.XZJK2019040。
文摘Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.
文摘创新是新时代医学教育改革发展的生命线。本文立足于“健康中国”建设大背景,介绍四川大学华西医院全科医学科实践经验和成果,结合医疗服务、管理、教育、学术能力四方面核心岗位胜任力的构建,以“全科-专科”和“医院-社区”协同整合规划轮转计划,训练家庭医生团队协作精神,融入亚专长全科医生(general practitioners with special interests,GPwSI)培养,并采用形成性评价和终结性评价相结合的多样化评估,创新与“健康中国”行动相适应的高效、可复制、可推广的全科医学专科医师规范化培训(以下简称“全科专培”)实施方案,以期为新形势下全科专培制度教学改革提供参考。
