基于正交独立成分分析的过程数据建模

针对非高斯数据分布过程中回归预测精度不足的问题，提出一种在独立成分分析（ICA）的基础上与正交信号校正（OSC）相结合的多元线性回归（MLR）方法——正交独立成分回归（O-ICR）．首先将原输入数据通过正交ICA（O-ICA）进行预处理，去除ICA在提取高阶统计量时带来的与Y无关的干扰变化，然后对校正后的X提取独立成分，代替原输入数据建立与Y之间的回归预测模型．与传统的ICR相比，该方法提取的独立成分经过校正可使回归模型的预测精度更高．最后通过Tennessee Eastman（TE）过程的质量预测仿真，验证了该建模方法的有效性．

Process Data Modeling Based on Orthogonal Independent Component Analysis

Based on independent component analysis（ICA), a multivariate linear regression（MLR) method combined with orthogonal signal correction（OSC), which is called orthogonal independent component regression（O-ICR), is proposed for regression prediction of non-Gaussian processes. First, the O-ICA is conducted on an original input data matrix for removing disturbing variation that is not correlated to Y from the extracted high-order statistics in ICA. Then, independent components are extracted X from after correction. The regression prediction model is derived using these components instead of the original input data and Y. Compared with the traditional ICR, the proposed method has a more superior performance because independent components are corrected. Finally, the validity of the method is verified though quality prediction simulation in the Tennessee Eastman（TE) process.