基于支持向量机的威胁判断模型

目标威胁判断是防空作战中一项重要内容,在建立目标威胁模型时,首先要挑选特征参数,分析了影响威胁度的若干因素.这里采用Rough理论中知识约简方法选择目标的特征参数;支持向量机是一类新型机器学习方法,由于其出色的学习能力,该技术已成为当前国际机器学习界的研究热点,利用支持向量机建立了威胁判断模型,给出了实例和解决此问题的支持向量机源程序.通过实例与神经网络法的结果进行了比较,结果表明支持向量机比较精确和简单.

Threat Estimation Model based on Support Vector Machine

The problem of estimating threat is important in air defence operation.On estimating threat of target,the first thing is to select the character parameters of target.Several factors,which affect the threat of target,are discussed.The character parameters of target are selected based on reduction of knowledge.Support Vector Machines are a kind of novel machine learning methods,which have become the hotspot of machine learning because of their excellent learning performance.A threat estimation model is established by using support vector machine.The method is illustrated through examples,and the source code is given also.The results obtained from support vector machine method are compared with that from neural network method.The results show that the support vector machine method is more accurate and simple than neural network method.