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 the Decision-tree SVM classifier

作　　者：洪建[1] 李臣明[1] 高红民[1] 

机构地区：[1]河海大学计算机与信息学院,南京210098

出　　处：《水资源与水工程学报》2017年第3期163-167,共5页Journal of Water Resources and Water Engineering

摘　　要：根据统计学理论提出一种基于决策树支持向量机的泵站状态识别方法,支持向量机是基于统计学理论发展而来的学习方法,在处理小样本,非线性,高维数的问题上较为出色。泵站系统数据往往维度较高,通过主成分分析将数据压缩降维,利用处理过后的数据对三级支持向量机进行训练结合决策树建立泵站运行状态判别模型,进行泵站稳态运行下的状态识别。实验表明:该方法优点是训练时间短,识别准确度高,具有较强鲁棒性。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 

分 类 号：TV93[水利工程—水利水电工程] S277.9[农业科学—农业水土工程]