摘要
为了提高蒸汽干度测量的精确性,提出了基人工蜂群优化最小二乘支持向量机的干度软测量模型。首先利用人工蜂群算法对最小二乘支持向量机的核参数进行参数优化,然后利用优化后的最小二乘支持向量机干度测量模型对干度进行软测量,软测量结果表明基于人工蜂群优化的最小二乘支持向量机的测量效果满足了精度要求。最后运用最小二乘支持向量机和BP神经网络模型对干度进行了软测量,结果表明:基于人工蜂群优化的最小二乘支持向量机软测量模型具有测量精度高,测量稳定性好的优点。
In order to improve the accuracy of measuring steam dryness,a least squares support vector machine（LSSVM） model optimized with artificial bee colony（ABC）algorithm was proposed,in which,having ABC algorithm used to optimize LSSVM’s parameters,and then having the improved LSSVM model employed to measure steam dryness.The soft-sensing results show that the improved LSSVM can meet accuracy requirements;and having both LSSVM and BP neural network measurement model applied to steam dryness measurement proves high efficiency and stability of ABC-LSSVM soft measurement model.
出处
《化工机械》
CAS
2013年第2期226-229,共4页
Chemical Engineering & Machinery
关键词
蒸汽干度
软测量
最小二乘支持向量机
人工蜂群算法
steam dryness
soft-sensing
least squares support vector machine
artificial bee colony algorithm
