摘要
提出了一种基于最小二乘支持向量机(LS-SVM)和灰色模型(GM)的钢球磨煤机料位动态软测量方法,分析了料位的影响因素,确定了软测量模型的辅助变量;基于LS-SVM建立料位软测量静态模型,将静态模型测量结果与实际值比较,获得测量误差时间序列,并采用GM对其建模和预测;将预测的误差结果与静态模型输出进行叠加,实现对测量结果的动态校正。实际应用结果表明,该方法能够有效地反映料位的变化趋势和动态特性,比单纯LS-SVM模型测量具有更高的精度和适用性。
A least squares support vector machine（LS-SVM）and grey model（GM）based dynamic soft sensor method for ball mills was proposed.By analyzing the factors affecting the coal level,the auxiliary variables of the soft sensor model were determined.The LS-SVM based soft sensor static model was established,of which the results were compared with that of the actual values.Thus the time measurement errors sequence was obtained and then modeled and predicted by the GM.Finally,the predictive error results were combined with the static model to realize dynamic correction.Application example shows this method can reflect the trend and dynamic characteristics of coal level effectively,which has a higher accuracy and applicability than the single LS-SVM model.
出处
《热力发电》
CAS
北大核心
2015年第1期77-81,共5页
Thermal Power Generation
基金
国家自然科学基金资助项目(50775035)
江苏省自然科学基金资助项目(BK2011391)
