Accelerated Recursive Feature Elimination Based on Support Vector Machine for Key Variable Identification

Key variable identification for classifications is related to many trouble-shooting problems in process industries. Recursive feature elimination based on support vector machine （SVM-RFE） has been proposed recently in application for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diagnosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee Eastman process （TEP） simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE （A-SVM-RFE）. A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis （PCA） and other two SVM-RFE algorithms, A-SVM-RFE performs better. It is more fitting for industrial application.     

支持向量机在线训练算法及其应用

针对支持向量机在线训练算法训练速度较慢和无法处理边缘支持向量集合为空的缺点,以KKT条件和拉格朗日乘数法为基础,用严格的数学推导得到一种改进的训练算法.通过建立一个矩阵缓存来保存与核函数相关的数据,给出在算法中有效操作该矩阵缓存的方法以加快训练速度;边缘支持向量集合为空时,修改模型的偏值项使样本进入该集合,训练算法得以继续运行;并讨论了该算法在在线系统辨识中的应用.仿真实验和分析结果表明: 对于非线性时变系统,改进算法的建模精度较高,训练速度较原算法有了很大的提高.     

基于2次核SVM的单步非线性模型预测控制

A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.     

打浆过程中成浆湿重的软测量技术研究

打浆过程是制浆造纸生产过程中极为重要的一环。本文着重就打浆过程中成浆湿重的软测量问题进行探讨。首先回顾了软测量的基本原理、主要作用及研究进展;在指出了对成浆湿重进行软测量的必要性之后,探讨了实现成浆湿重软测量的可能性;最后根据现代打浆理论和软测量的原理,给出了具体的实现方法。