Based on the analysis of some approaches of component variability, this paper discusses the variability implementation approach by using design pattern.
在分析已有的构件可变性实现方法的基础上，本文提出了使用设计模式实现构件可变性的方法。
Methods Chemical pattern recognition was performed on the fruits of Ligustrum lucidum collected from various areas of China by PCA (principal component analysis) method.
方法应用主成分分析法(PCA)对来源于全国不同产地的女贞子进行了化学模式识别研究。
The principal component analysis method was adopted to simplify the training pattern of neural network, and optimize the structure of the network.
采用主成分分析方法简化神经网络训练样本，进而优化网络的结构。
Principal component analysis (PCA), cluster analysis (CA) and back-propagation artificial neural network (BP-ANN) were used in the data analysis and pattern recognition.
通过主成分分析、聚类分析和BP神经网络对实验数据进行了分析和识别。
Principal component analysis (PCA), cluster analysis (CA) and back-propagation artificial neural network (BP-ANN) were used in the data analysis and pattern recognition.
通过主成分分析、聚类分析和BP神经网络对实验数据进行了分析和识别。
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