The success or failure of practical teaching in data structures and algorithms coxirse determines the success or failure of the course group.The reform of practical teaching in this course takes the course group as th...The success or failure of practical teaching in data structures and algorithms coxirse determines the success or failure of the course group.The reform of practical teaching in this course takes the course group as the background and aims to meet the needs of students at different levels.It proposes a hierarchical practical teaching mode involving three levels in terms of foundation,design,and synthesis driven by cases throughout the whole course group and a fine process control mechanism based on multi-stage process assessment.展开更多
针对目标编群中单一算法存在的适用范围小、误分率高的问题,提出一种新的态势估计中目标编群的处理方法。首先应用Hop fie ld神经网络对态势中目标的目的地做出判断,然后采用多相似性加权策略计算出目标间的相关系数,再根据最大相关系...针对目标编群中单一算法存在的适用范围小、误分率高的问题,提出一种新的态势估计中目标编群的处理方法。首先应用Hop fie ld神经网络对态势中目标的目的地做出判断,然后采用多相似性加权策略计算出目标间的相关系数,再根据最大相关系数层次聚类算法实现编群。仿真结果表明方法能在一定程度上减小错误编群的概率,同时适用范围也得到了扩展。展开更多
文摘The success or failure of practical teaching in data structures and algorithms coxirse determines the success or failure of the course group.The reform of practical teaching in this course takes the course group as the background and aims to meet the needs of students at different levels.It proposes a hierarchical practical teaching mode involving three levels in terms of foundation,design,and synthesis driven by cases throughout the whole course group and a fine process control mechanism based on multi-stage process assessment.
文摘针对目标编群中单一算法存在的适用范围小、误分率高的问题,提出一种新的态势估计中目标编群的处理方法。首先应用Hop fie ld神经网络对态势中目标的目的地做出判断,然后采用多相似性加权策略计算出目标间的相关系数,再根据最大相关系数层次聚类算法实现编群。仿真结果表明方法能在一定程度上减小错误编群的概率,同时适用范围也得到了扩展。