SVM在气体减压器稳定性能中的应用

使用支持向量机(SVM)研究涡轮气封减压试验系统中高压卸荷膜片式减压器的稳定性问题,主要集中于以往方法不易涉及的多结构参数变化.针对稀疏易有残缺的小样本空间,与BP(back propagation)神经网络模型进行对比,得出SVM方法在所研究数据集上的一些结论:SVM模型预测性能在多结构参数变化情形下优于BP神经网络模型,预测误差平均降低了25.5%;SVM的泛化性好于BP;在双参数、三参数情形下,SVM模型为气体减压器的设计提供了更好的决策支持,给出了优化结构参数的设计建议. 

Application of SVM in stability of gas pressure reducing regulator

Support vector machine (SVM) was used to study the stability problem of high pressure unloading diaphragm pressure reducing regulator in the turbine gas seal pressure reducing test system. The research mainly focused on multi-structural parameters variation, which was difficult to solve by the classical methods. By comparing SVM and back propagation (BP) neural network model in the scarce and incomplete data set, several conclusions were proposed: the prediction models of SVM can get 25.5% less error in average and are superior to BP neural network model in the situation of multi-structural parameters variation; the generalization of SVM is also better than BP's in sparse samples space; suggestions of adjusting parameters are given for achieving good stability, and better decision-making is provided by SVM models in design of pressure reducing regulator,with double parameter combinations and tri-parameter combinations.