目标机动识别是空战态势感知中的关键问题.针对现有识别方法主观因素较多、模型复杂、难以满足实时性和识别准确率不够高等问题,提出一种基于运动分解和层次支持向量机(hierarchical support vector machine,H-SVM)的机动识别方法.利用v...目标机动识别是空战态势感知中的关键问题.针对现有识别方法主观因素较多、模型复杂、难以满足实时性和识别准确率不够高等问题,提出一种基于运动分解和层次支持向量机(hierarchical support vector machine,H-SVM)的机动识别方法.利用v-SVM二分类器构造H-SVM多分类器.结合运动分解思想,提出从不同方向对目标机动动作进行分解识别的方法,简化识别过程的同时增强识别的针对性.选取空战训练测量仪(air combat maneuvering instrument,ACMI)中的实测空战训练数据并构造机动识别样本数据,对识别模型进行训练,并通过实例仿真分析不同算法机动识别的性能.结果表明,所提出的识别方法具有较高的准确性和实时性,可以对战斗机的各类机动动作进行准确、快速地识别.展开更多
为了分析多类支持向量机(Multi-category support vector machines,M-SVMs)的推广性能,对常用的M-SVMs算法加以概述,推导、总结了理论推广误差公式。对于给定的样本集,可以设计合理的编码来提高ECOCSVMs的推广性能,通过构造合理的层次...为了分析多类支持向量机(Multi-category support vector machines,M-SVMs)的推广性能,对常用的M-SVMs算法加以概述,推导、总结了理论推广误差公式。对于给定的样本集,可以设计合理的编码来提高ECOCSVMs的推广性能,通过构造合理的层次结构来提高H-SVMs推广性能,其余M-SVMs算法的推广性能均取决于样本空间。研究结果为有效使用M-SVMs提供了依据,为改进M-SVMs指明了方向。展开更多
To recognize the presence of the headstream of gushing water in coal mines, the SVM (Support Vector Ma- chine) was proposed to analyze the gushing water based on hydrogeochemical methods. First, the SVM model for head...To recognize the presence of the headstream of gushing water in coal mines, the SVM (Support Vector Ma- chine) was proposed to analyze the gushing water based on hydrogeochemical methods. First, the SVM model for head- stream analysis was trained on the water sample of available headstreams, and then we used this to predict the unknown samples, which were validated in practice by comparing the predicted results with the actual results. The experimental results show that the SVM is a feasible method to differentiate between two headstreams and the H-SVMs (Hierachical SVMs) is a preferable way to deal with the problem of multi-headstreams. Compared with other methods, the SVM is based on a strict mathematical theory with a simple structure and good generalization properties. As well, the support vector W in the decision function can describe the weights of the recognition factors of water samples, which is very important for the analysis of headstreams of gushing water in coal mines.展开更多
文摘为了分析多类支持向量机(Multi-category support vector machines,M-SVMs)的推广性能,对常用的M-SVMs算法加以概述,推导、总结了理论推广误差公式。对于给定的样本集,可以设计合理的编码来提高ECOCSVMs的推广性能,通过构造合理的层次结构来提高H-SVMs推广性能,其余M-SVMs算法的推广性能均取决于样本空间。研究结果为有效使用M-SVMs提供了依据,为改进M-SVMs指明了方向。
基金Project 40401038 supported by the National Natural Science Foundation of China and 2003047 by the Top 100 Outstanding Doctoral Dissertation Foun-dation of China
文摘To recognize the presence of the headstream of gushing water in coal mines, the SVM (Support Vector Ma- chine) was proposed to analyze the gushing water based on hydrogeochemical methods. First, the SVM model for head- stream analysis was trained on the water sample of available headstreams, and then we used this to predict the unknown samples, which were validated in practice by comparing the predicted results with the actual results. The experimental results show that the SVM is a feasible method to differentiate between two headstreams and the H-SVMs (Hierachical SVMs) is a preferable way to deal with the problem of multi-headstreams. Compared with other methods, the SVM is based on a strict mathematical theory with a simple structure and good generalization properties. As well, the support vector W in the decision function can describe the weights of the recognition factors of water samples, which is very important for the analysis of headstreams of gushing water in coal mines.
