水资源与水工程学报2017，Vol.28Issue(3)：163-167,5.DOI:10.11705/j.issn.1672-643X.2017.03.30
基于支持向量机决策树的泵站稳态工况运行下状态识别
State recognition of the running pump station based on theDecision-tree SVM classifier
洪建 1李臣明 1高红民1
作者信息
- 1. 河海大学 计算机与信息学院,南京 210098
- 折叠
摘要
Abstract
A Decision-tree SVM classifier is applied to the state recognition of the running pump station based on statistical learning theory(SLT).SVM is a novel machine learning method based on SLT and powerful for the problems with small sample, nonlinear and high dimension.The data of pump station system tends to have higher dimension, and the data is dimensioned down by principal component analysis.The Decision-tree SVM classifier, trained with the sampling data from the above dealing process and forming an identification model, identifies the state of the pump station.The test results show that the proposed classifier has an excellent performance on correcting ratio and training speed.关键词
泵站/状态识别/支持向量机/决策树Key words
pump station/state recognition/support vector machine (SVM)/decision-tree分类
建筑与水利引用本文复制引用
洪建,李臣明,高红民..基于支持向量机决策树的泵站稳态工况运行下状态识别[J].水资源与水工程学报,2017,28(3):163-167,5.
