针对间歇过程过渡状态下具有的复杂过程特性，提出一种基于二维动态主成分分析(2ddpca)的故障诊断方法。
A novel modeling method, two-dimensional dynamic principal component analysis (2ddpca), was developed for monitoring batch process transition in author's previous work.
河南省；粮食生产动态变化；主成分分析。
Henan Province; grain production; dynamic variety; Principal Component Analysis.
对多维动态数据系统给出了全局主成分分析(GPCA)模型，并对时序立体数据表进行立体式的综合与简化。
Global principal components analysis (GPCA) model is presented to the multidimensional dynamic data system and the time sequence solid data table is three-dimensionally synthesized and simplified.
本文运用主成分分析方法动态构造模板，运用仿射变换的方法匹配定位，从而实现稳定的跟踪。
It is proposed to reconstruct model dynamically and using the method of affine transform to match. So we can realize stable tracking.
本文提出一种基于主成分分析法的动态神经网络模型实现高炉铁水含硅量多步预报。
On the basis of this, this paper suggests that analytic hierarchy process(AHP) and principle component analysis(PCA) can .
本文提出一种基于主成分分析法的动态神经网络模型实现高炉铁水含硅量多步预报。
On the basis of this, this paper suggests that analytic hierarchy process(AHP) and principle component analysis(PCA) can .
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