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中文摘要：
为改善软测量模型精度，提出了一种局部惩罚加权核偏最小二乘算法。该方法通过核映射将原始输入映射到高维特征空间实现对非线性问题的线性化处理，并通过偏最小二乘算法进行主成分提取，降低数据维数；对由主成分构成的新数据集，依据局部学习思想构建局部惩罚加权最小二乘回归模型，降低模型对异常数据的敏感度、优化模型参数。鉴于多模型可以改善模型估计精度，提高泛化性，采用C—NN近邻扩张搜索聚类算法对样本集进行聚类，对得到的聚类子簇依据上述算法建立回归子模型，得到多模型软测量系统。将其应用于双酚A生产过程的质量指标软测量建模，仿真结果表明了方法的有效性。
英文摘要：
In order to improve the accuracy of soft-sensor model, a novel penalized weighted kernel partial least squares algorithm is presented. The original inputs are mapped into a high dimensional feature space to realize the linearization of nonlinear problems. The partial least squares algorithm is used to extract the principal component to reduce the dimensional of data. According to the local learning theory, a local penalized weighted least squares regression model is constructed based on the new data set formed by the principal component. The model sensitivity of abnormal data is reduced and the model parameters are optimized. The C-NN expanding search clustering method is used to cluster the sample set, and the regression sub-models are established in the light of the sub-cluster respectively. A soft sensor system based on multiple models is obtained. The method is used in a soft sensor model for the Bisphenol-A productive process, and the simulation result shows the effectiveness of the algorithm.
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