孙茂伟, 杨慧中. 局部加权混合核偏最小二乘算法及其在软测量中的应用[J]. 信息与控制, 2015, 44(4): 481-486. DOI: 10.13976/j.cnki.xk.2015.0481
引用本文: 孙茂伟, 杨慧中. 局部加权混合核偏最小二乘算法及其在软测量中的应用[J]. 信息与控制, 2015, 44(4): 481-486. DOI: 10.13976/j.cnki.xk.2015.0481
SUN Maowei, YANG Huizhong. Local Weighted Mixed Kernel Partial Least Squares Algorithm and Its Applications in to Soft-sensing[J]. INFORMATION AND CONTROL, 2015, 44(4): 481-486. DOI: 10.13976/j.cnki.xk.2015.0481
Citation: SUN Maowei, YANG Huizhong. Local Weighted Mixed Kernel Partial Least Squares Algorithm and Its Applications in to Soft-sensing[J]. INFORMATION AND CONTROL, 2015, 44(4): 481-486. DOI: 10.13976/j.cnki.xk.2015.0481

局部加权混合核偏最小二乘算法及其在软测量中的应用

Local Weighted Mixed Kernel Partial Least Squares Algorithm and Its Applications in to Soft-sensing

  • 摘要: 为提高基于核函数的偏最小二乘算法非线性处理能力,削弱软测量模型对异常数据的敏感度,提高模型泛化能力,提出一种用于软测量在线建模的局部加权混合核偏最小二乘算法.该算法以多个具有不同特性的单一核函数构成混合核函数,将原始输入映射到高维特征空间,再采用局部加权学习算法在高维特征空间中计算样本权值,并对核变换后的样本数据进行加权处理,然后采用核函数偏最小二乘算法建立在线局部软测量模型.通过数值仿真和采用来自工业双酚A生产装置的现场数据进行在线软测量建模仿真,结果证明该算法是有效的.

     

    Abstract: To improve the nonlinear processing capacity of the partial least-squares algorithm based on a kernel function, reduce the sensitivity of the soft-sensor model to abnormal data, and improve the generalization ability of this model, we present a local weighted mixed-kernel partial least squares algorithm for soft-sensing online modeling. The original inputs are mapped into a high-dimensional feature space via a mixed-kernel function comprising several kernel functions with different properties. In the high-dimensional feature space, the mapped data are weighted according to the weight of each sample calculated by a locally weighted learning algorithm. The kernel partial least squares algorithm is then used to establish an online local soft-sensing model. A numerical simulation and an online soft-sensor modeling simulation using data related to industrial bisphenol-A production units is used to demonstrate the effectiveness of the proposed algorithm.

     

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