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1·The normalized vector is used as the input of RBF NN, and target recognition is performed based on this, which leads to a satisfactory recognition result.

并对该特征向量进行对数归一化，将归一化的特征向量作为径向基函数（RBF）神经网络的输入，在此基础上进行识别，达到较好的识别效果。

2·Using statistical methods to calculate the Angle interval mean and variance of pixels to centroid distance of the normalized gait silhouette images, and construct them as a feature vector.

用统计学的方法，等角度间隔地计算归一化步态轮廓图像各像素点至质心距离的均值与方差，并用其构造步态识别的特征向量。

3·No convergent drawback of the weight vector due to choose the dispersion constant inaccurately in the normalized constant modulus algorithms (NCMA) is overcome.

该算法克服了在一般的常系数盲数字自适应算法中，由于估计幅值选择不合适，所造成加权矢量不收敛的缺点。

4·Furthermore, with the help of the normalized decision-making matrix and the object weight vector, the comprehensive evaluations of the alternatives were worked out.

通过规范化决策矩阵和目标权重向量得到各方案的综合评价值；

5·This paper presents a new SAR raw data compression algorithm named Normalized Adaptive Predictive Vector Quantization (NAPVQ).

该文提出归一化自适应预测矢量量化(NAPVQ)算法压缩sar原始数据。

6·Will be extracted after the six feature vector for normalized.

之后将提取出来的六个特征向量进行归一化处理。