提出了以变速箱振动信号的时序模型为基础的3个特征向量参数，并以此建立标准模式特征向量；
Three characteristic vectors based on the time series model of gearbox's vibrating signals are suggested and distinguishing functions are set up with these three vectors.
特征提取的依据是利用有关数学工具，去掉对分类无用的信息，寻找最有效的信号特征来构成用于分类识别的模式特征向量。
The extracting process is to throw off the useless information and look for the most efficient signal feature to form a pattern feature vectors for classification with mathematics tools.
本文探讨了用神经网络从模式中自动抽取特征向量并确定特征向量是否已具有足够特征信息的方法，给出了计算机模拟的结果。
In this paper, we have studied the method of drawing automatically from pattern and assessing whether feature vectors contain enough feature information. The result of computer simulation is given.
广义特征向量表达了锅炉系统运行参数与运行工况数间的数值对应关系，因而决定了系统的一种特定污染模式。
The generalized eigenvector shows the relation between operation parameters and the values of work condition for a boiler, at the same time, one kind of particular pollution pattern is presented.
介绍了一种基于模糊模式识别以及向量空间模型提取特征向量的中文文本分类器的设计与实现。
This paper introduces the design and implementation of the Chinese text categorizer based on the fuzzy recognition and the extraction of the characteristic vector with the vector space model.
利用本文之有限元素分析模式与级数解模式，与求解特征值与特征向量的方法，可以求得功能梯度板之自然振动频率、振动模态与挫屈负荷。
The present finite element model and the series solution models are used to find the natural frequencies, vibration modes and buckling loads of the FGM plates.
通过将对分类有相同贡献的文本特征词聚合，使用它们共同的分类贡献向量特征模式作为文本特征向量的基本维；
Multiple discriminating features with similar contribution to classification are combined into one pattern, which is used as the basic feature dimension.
最后，通过计算规则和模式之间的兼容性指标来构造特征向量，构建支持向量机的分类器模型。
Eventually, the compatibility between the generated rules and patterns was used to construct a set of feature vectors, which were used to generate a classifier.
用神经网络来进行模式识别的一个关键步骤就是要选取有代表性的特征向量。
A key step of using neural networks for pattern recognition is to choose typical eigenvector.
用神经网络来进行模式识别的一个关键步骤就是要选取有代表性的特征向量。
A key step of using neural networks for pattern recognition is to choose typical eigenvector.
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