Nonlinear Soft Sensor Development Based on Relevance Vector Machine
Nonlinear Soft Sensor Development Based on Relevance Vector Machine
复制标题
基于相关向量机的非线性软测量开发
DOI：
10.1021/ie101146d
复制
发表时间：
2010-08
影响因子：
4.2
通讯作者：
Song, Zhihuan
中科院分区：
文献类型：
--
作者：
Ge, Zhiqiang;Song, Zhihuan
本文提出了一种基于相关向量机（RVM）的有效非线性软传感器，该方法最初是在机器学习领域提出的。与广泛使用的支持向量机（SVM）和基于最小二乘支持向量机（LSSVM）的软传感器相比，RVM提供了更稀疏的模型结构，可以大大降低在线预测的计算复杂度。SVM/LSSVM只能提供预测结果的点估计，而RVM给出的是概率预测结果，对于软测量应用来说更为复杂。此外，RVM成功地避免了传统支持向量机类型方法的核函数限制、参数调优复杂性等缺点。由于RVM的优点，本文将该方法应用于软传感器建模。为了评估所开发的软传感器的性能，演示了两个案例研究，它们都支持RVM比其他软测量方法表现得更好。
This paper proposes an effective nonlinear soft sensor based on relevance vector machine (RVM), which was originally proposed in the machine learning area. Compared to the widely used support vector machine (SVM) and least-squares support vector machine (LSSVM) based soft sensors, RVM gives a more sparse model structure, which can greatly reduce computational complexity for online prediction. While SVM/LSSVM can only provide a point estimation of the prediction result, RVM gives a probabilistic prediction result, which is more sophisticated for the soft sensor application. Furthermore, RVM can successfully avoid several drawbacks of the traditional support vector machine type method, such as kernel function limitation, parameter tuning complexity, and etc. Due to the advantages of RVM, a practical application of this method is made for soft sensor modeling in this paper. To evaluate the performance of the developed soft sensor, two case studies are demonstrated, which both support that RVM performs much better than other methods for soft sensing.