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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。“,”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 pre-diction model is derived using these components instead of the original input data and Y.Com-pared with the traditional ICR,the proposed method has a more superior performance because in-dependent components are corrected.Finally,the validity of the method is verified though quality prediction simulation in the Tennessee Eastman (TE)process.