线性模型

W. H. Finch, Jocelyn E. Bolin, Ken Kelley
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引用次数: 0

摘要

在假设几个变量的经验值与“真”不相同的情况下,它们的一致性变化。这种情况的出现,例如,由于测量设备的误差,经验数据注册的存在。作为一个典型的例子,您可以在确定输入-输出线性模型的系数时指定识别任务。您可能还会注意到,在训练阶段获得的估值参数被用于处理在不同时间间隔记录的数据,例如在模式识别或自补偿干扰问题中。在某些情况下,我们可以讨论所研究过程的相互作用(关系)模型。探讨了在这些条件下,经验值的正交投影原理在由一组被分析的“真”值所定义的超平面上应用的可行性。获得了估计一致性线性模型参数的基本计算公式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Linear Models
consistency changes of several variables on the assumption that their empirical values are not identical to "true". Such situations arise, for example, because of the presence of errors registration of empirical data, due to the measuring equipment. As a typical example, you can specify the task of identification when determining the coefficients of the linear model of input -output. You may also notice a situation when valuation parameters obtained during the training phase, are used in the processing of data recorded in different time intervals, for example in the problems of pattern recognition or self-compensation interference. In some cases we can speak about models of the interaction (relationship) of the studied processes. Explore the feasibility of using in these conditions the principle of orthogonal projecting of empirical values on the hyperplane defined by a set of analyzed the "true" values. Received basic computational formulas for estimating the parameters of linear models of consistency.
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