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.
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 abil- ity of this model, we present a local weighted mixed-kernel partial least squares algorithm for soft-sensing on- line modeling. The original inputs are mapped into a high-dimensional feature space via a mixed-kernel func- tion 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 al- gorithm. The kernel partial least squares algorithm is then used to establish an online local soft-sensing mod- el. A numerical simulation and an online soft-sensor modeling simulation using data related to industrial bis- phenol-A production units is used to demonstrate the effectiveness of the proposed algorithm.
出处
《信息与控制》
CSCD
北大核心
2015年第4期481-486,共6页
Information and Control
基金
国家自然科学基金资助项目(61273070)
江苏省高校优势学科建设工程资助项目
关键词
核偏最小二乘
混合核函数
局部加权混合核偏最小二乘
软测量
kernel partial least squares
mixed kernel function
local weighted mixed kernelpartial least squares
soft-sensing
