一种新的间歇过程多模式辨识方法

A New Multi-mode Identification Method of Batch Process

针对间歇过程的多模式划分问题,提出了一种基于主角度相似度比较的多模式划分新方法,有效克服了噪声或冗余数据对模式划分的影响.该方法的基本思想是利用PCA对间歇数据按时间轴进行主成分建模,然后利用主角度这一用于比较子空间相似度的方法进行主元模型相似度比较,从而对各个模型和过渡过程进行有效辨识和划分;在此基础上,对上述方法进行了深入分析,改进并完善了主角度相似度划分标准,使这一方法更趋完善.仿真结果检验了所提方法的有效性.

A new method of multi-mode partition in the batch processes was presented on the basis of comparing the similarity of principal angles, which effectively overcomes the defects of noise or redundant data impaction on the phase partition. The main idea is to set up the principal component models of the batch data along time axis by employing the principal component analysis （PCA） approach, and then uses the principal angle method to compare the similarity of the principal component models, finally identify the steady phases and the transition phases effectively. Furthermore, an improved strategy is proposed to complete the partition criterion of the principal angle similarity. The simulation results demonstrate the effectiveness of the proposed method.