基于非线性多向ICA的间歇过程监控方法研究  

Monitoring Method Based on Nonlinear Multi-way ICA for Batch Process

作　　者：张晓玲[1] 田学民[1] 

机构地区：[1]中国石油大学(华东)信息与控制工程学院,东营257061

出　　处：《系统仿真学报》2009年第11期3365-3369,共5页Journal of System Simulation

基　　金：国家863资助项目(2004AA412050);山东省自然基金(Y2007G49)

摘　　要：针对间歇过程的非线性操作特性,提出一种非线性的多向独立成分分析(MICA)方法-基于特征样本的多向核独立成分分析(FS-MKICA)。该方法首先把正常工况下的间歇过程三维数据展开成二维,对输入的二维训练样本集进行特征样本提取,然后利用核函数完成从非线性特征样本输入空间到线性高维空间的转换,在变换后的线性高维空间中用独立成分分析(ICA)法提取独立成分构建模型。FS-MKICA不仅提取了过程的非线性特性,且避免了直接对全体输入样本建模,降低了计算复杂性。将FS-MKICA用于监视青霉素发酵过程,仿真结果验证了该方法是有效的。Most batch processes generally exhibit the feature of nonlinear variation. A nonlinear multi-way independent component analysis （MICA） technique was proposed that is multi-way kernel independent component analysis based on feature samples （FS-MKICA） method. This approach first makes three-way datasets of normal batch processes unfolded to be two-way and then chooses feature samples from the large two-way input training datasets. The nonlinear feature space is then transformed to high-dimensional linear space via kernel function and independent component analysis （ICA） model is established in the linear space. FS-MKICA not only extracts the nonlinear feature of batch processes, but also reduces the computational cost based on whole input samples. The simulation results in monitoring fed-batch penicillin fermentation show that FS-MKICA method is effective.

关 键 词：间歇过程 非线性 特征样本 核函数 多向独立成分分析 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]