本文以部分最小二乘法(Partial Least Squares, PLS)为基础,针对TE 过程中的21 个故障数据中的几个作为重点研究对象,进行故障建模。
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药师之友 mponeunts,PC)法,适应最小二乘法(adaptive least-squares,ALS)及部分最小二乘法(partial least square,PLS)等。 模式识别法尚存在一些问题,如用有限的样本数据要把多维空间截然分开是不容易的。同时,用来分类的
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和部分最小二乘回归法 Partial-Least-Squares Regression
然后把摄像机内参数矩阵分解为有效焦距与主点位置两部分,并利用最小二乘法分别对其进行求解。
The intrinsic parameters are separated into two parts of focal length and coordinates for the principle point, which are then solved by using the least square method.
线性网络部分的参数采用递推最小二乘法辨识,多层前向网络的权值和阈值采用BP算法学习。
The parameters of linear network are identified by recursive least square and weights and thresholds of MFNN are learned by BP algorithm.
对观测到的谱线利用最小二乘法进行了拟合,得到了部分分子常数。
The obtained lines have been least-squares fitted and molecular constants have been obtained.
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