A multi-model approach for soft sensor development based on feature extraction using weighted kernel fisher criterion简

Multi-model approach can significantly improve the prediction performance of soft sensors in the proc- ess with multiple operational conditions. However, traditional clustering algorithms may result in overlapping phe- nomenon in subclasses, so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory. In order to solve these problems, a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy, in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses. Furthermore, the classified data are used to develop a multiple model based on support vector machine. The proposed method is applied to a bisphenol A production process for prediction of the quality index. The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.     

基于环路能量聚类的多模型软测量建模

在多模型软测量建模中,对于新的数据以及异常样本点,传统的聚类方法没有充分考虑它们的特性,因而所属类别往往不能反映其真实属性,最终导致模型精度不高.为此,提出一种基于最小环路能量聚类的算法,该方法将样本聚类转化为寻找一个最小能量环问题,通过模拟退火算法搜索一条经过所有样本点的最小能量环实现样本集的聚类;对侦破出的异常样本点和新的测试数据根据其能量值确定其所属属性,从而提高聚类和分类精度;然后利用支持向量机为各个子类建立回归子模型,得到软测量组合模型.将该方法应用于双酚A生产过程质量指标的软测量建模中,仿真结果验证了该方法的有效性.     

基于类别特征提取的组合支持向量机模型

在建立复杂生产过程软测量模型时,使用单一的支持向量机模型或基于传统聚类方法的组合支持向量机模型有时难以很好地跟踪突变信号或取得满意的泛化效果.为解决这个问题提出了一种改进的线性判别分析算法.该算法结合类边界分析得到类别的特征向量,利用该特征向量将数据变换后分别建立支持向量机子模型,并用各组特征向量中有效特征值之和构建各...