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1)  block variables
块变量
1.
Such new variable is tested by pattern recognition for saturated alcohol and ether, combined with block variables' orthogonal computation and canonical correlation ana.
联合块变量正交化和典型相关分析方法,以饱和醇醚的模式识别模型对变量进行了验证。
2.
Based on block variables thought, available information about classification was drawn from several series of classifers by orthogonal computation and canonical correlation analysis(CCA), the discriminant least square models were thus set up to classify these ether and alcohol samples.
对NIST6 2库中所包含的 377种饱和醇醚进行化学模式识别研究 ,基于块变量思想 ,将正交化和典型相关分析的方法应用于几类常见的质谱分类变量以提取分类信息 ,并由此建立最小二乘判别式分类模型。
2)  Varing-mass block-body
变质量块体
3)  block-featuring variant
块特征变量
1.
For the temporal correlation, each frame was segmented into three kinds of regions according to block-featuring variant TL , and correlation in each region was analyzed.
对于空域相关性,根据块特征变量(TL)将图像分成三类区域,分别分析每个区域内部的视差矢量相关性。
4)  module invariant
模块不变量
5)  Modular multivariable control
模块多变量控制
1.
To solve constraint control problem, this paper combines modular multivariable control and self rectifying controller, besides stair like control, to form a new multivariable self tuning controller.
为了解决控制中的约束问题 ,将模块多变量控制思想与自校正控制器相结合 ,并引入阶梯式控制 ,构造一种新的自校正控制器 。
2.
From the point of constraint control, this paper constructs a new multivariable self tuning regulator combining modular multivariable control technique and self tuning regulator.
从约束控制的角度出发 ,将模块多变量控制与自校正调节器相结合 ,构造出一种新的多变量自校正调节器 ,并对此进行了仿真研
6)  modular multivariable optimization
模块多变量优化
补充资料:变量与变量值
可变的数量标志和所有的统计指标称作变量。变量的数值表现称作
变量值,即标志值或指标值。变量与变量值不能误用。
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
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