基于集成多支持向量回归融合的上浆率在线软测量方法

现有浆纱过程上浆率的检测无法实现实时在线测量，直接影响纱线产品质量的保障。本文提出一种新的基于Bagging多SVR融合的建模方法，建立上浆率在线软测量模型。首先对浆纱过程进行分析，确定影响上浆率的主要因素。将这些主要因素作为模型的输入，用不同的核函数、损失函数和参数建立基本SVR模型。使用Bagging将多个SVR模型进行融合，使他们的优势进行互补，不足得以克服，得到最终的上浆率在线软测量模型。使用实际生产数据对模型进行检验，并将其与传统软测量方法进行比较，结果表明基于Bagging多SVR融合的上浆率在线软测量模型的性能优于传统软测量模型，并具有较高的测量精度，完全能够满足实际生产的需要。

Online soft measurement of sizing percentage based on intergrated multiple SVR fusion by Bagging

The measure method for sizing percentage is not real time method in practical production process. Therefore the yarn quality can not be guaranteed. A new modeling method is proposed for sizing percentage soft sensor real time based on multiple SVR fusion by bagging. Firstly, the main factors that influence sizing percentage are obtained by analyzing the sizing process. Then the basic SVR soft sensor models are built by different kernel functions, different loss functions and different parameters. Finally, the different basic SVR models are integrated by bagging. The basic SVR can learn from each other by this way. The new sizing percentage soft sensor model is established. The practical production data are used to test the new soft sensor model. And the traditional soft senor methods based on single BP neural network and single SVR are used to compare. The results demonstrate that the new method has the best performance. The accuracy of new soft sensor can meet the needs of practical production.