摘要： 在指控系统网络维修保障中,针对健康评估过程中主观性强、实时性不高等问题,为减弱人为因素影响,通过分析支持向量机分类的几何意义,将指控网络健康评估问题转化为分类问题。采用优化的样本支持向量机对样本点进行选择优化,从而使样本分类速度和分类性能得到提升。在定量分析到定性健康状态描述的对应关系中,通过云模型实现不确定信息的转换。实验结果表明,由改进的指控网络模型得到的仿真结果与实际情况较为符合,分类性能较高。

关键词: 指控网络, 健康评估, 支持向量机, 快速分类, 优化样本, 云模型

Abstract: In the network maintenance support of the command and control system,aiming at the problem of strong subjectivity and low real time property in the process of health assessment,to reduce the influence of human factors,by analyzing the geometric significance of Support Vector Machine(SVM) classification,the problem of network health assessment is transformed into a classification problem.Optimizing Samples-SVM(OS-SVM) is used to optimize the sample points,which can improve the classification speed and classification performance of samples.In the correspondence between quantitative analysis and qualitative description of health status,the cloud model is used to realize the transformation of uncertain information.Experimental results show that the simulation results obtained by the improved command and control network model are in good agreement with the actual situation,and the classification performance is higher.

Key words: command and control network, health assessment, Support Vector Machine(SVM), fast classification, optimized sample, cloud model

中图分类号: 

• TP393