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针对工业软测量中的非线性数据回归问题,提出一种基于特征向量提取的核回归建模方法。基于核函数非线性变换技术,建立非线性软测量模型—核回归模型。为了减少核回归模型中的优化参数,采用特征向量提取(FVS)算法选择核回归模型的特征向量,最后采用改进的粒子群优化算法估计模型参数。在工业数据上的应用结果说明了方法的有效性。
Aiming at the problem of nonlinear data regression in industrial soft sensing, a kernel regression modeling method based on eigenvector extraction is proposed. Based on the kernel function nonlinear transformation technology, a nonlinear soft sensor model - nuclear regression model is established. In order to reduce the optimization parameters in the nuclear regression model, the eigenvector extraction (FVS) algorithm was used to select the eigenvectors of the kernel regression model. Finally, the improved particle swarm optimization algorithm was used to estimate the model parameters. The application results on industrial data illustrate the effectiveness of the method.