摘要
为了直接反映可控边界参数与热耗率的映射关系,基于υ-SVM建立了可控边界参数与热耗率的回归模型,选取与热耗率关联性强的可控边界参数作为输入参数,并应用灰色关联度模型进行验证,详细地描述了基于Libsvm软件建立υ-SVM回归模型的过程,并与BP神经网络模型进行对比.结果表明:在小样本情况下,υ-SVM模型回归精度更高,具有更好的泛化能力;在输入参数小幅波动的情况下,υ-SVM模型的输出结果基本稳定,具有很好的鲁棒性,满足实际应用的精度要求.
To directly reflect the mapping relation between controllable boundary parameters and heat rate of steam turbine,a regression model based onυ-SVM was established by taking the controllable boundary parameters with strong relevance with heat rate as the input parameters,which was subsequently verified using gray correlation degree model.The process of establishingυ-SVM regression model based on Libsvm software was described in detail,and the new model was compared with that of the BP neural network.Results show that under small-sample circumstances,the υ-SVM model has higher regression precision and better generalization capability;whereas under slight fluctuation of input parameters,the outputs of υ-SVM model are basically stable,indicating that the model has good robustness and can meet precision requirement of actual applications.
出处
《动力工程学报》
CAS
CSCD
北大核心
2014年第8期606-611,645,共7页
Journal of Chinese Society of Power Engineering
关键词
ν-SVM
支持向量机
汽轮机
热耗率
回归模型
可控边界参数
υ-SVM
support vector machine
steam turbine
heat rate
regression model
controllable boundary parameter
