APPLICATION OF GENERALIZED LINEAR FILTERS IN DATA-ANALYSIS
APPLICATION OF GENERALIZED LINEAR FILTERS IN DATA-ANALYSIS
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DOI：
10.1007/bf02188681
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发表时间：
1994-07-01
影响因子：
1.6
通讯作者：
SHUKLA, A
中科院分区：
文献类型：
--
作者：
BARNES, SE;PETER, M;SHUKLA, A
它表明，一个有用的广义线性滤波器W可以构造从实验数据。将数据分成多个实验，用这个集合来计算出现在W。反过来，从这个过滤器确定一个“哈密尔顿”H。该哈密顿量的特征向量和特征值的评估。对于“好”的实验，有一个小的特征值，其余的特征值约为1。如此确定的W'有效地减少了新数据集中的噪声。两个或更多小特征值的存在表明实验数据包含不止一个信号。W对系综的所选成员和/或新数据集的作用提取了不同的信号，同样具有有用的降噪。计算机模拟和真实的正电子湮没数据都被用来说明这些发展。
It is shown that a useful generalized linear filter W can be constructed from experimental data. The data are divided into many experiments and this ensemble is used to calculate the autocorrelation functions which appear in W. In turn, from this filter one determines a ''Hamiltonian'' H. The eigenvectors and eigenvalues of this Hamiltonian are evaluated. For a ''good'' experiment there is one small eigenvalue, and the rest are approximately 1. The W' so determined usefully reduces the noise in a new data set. The presence of two or more small eigenvalues indicates that the experimental data contains more than a single signal. The action of W on selected members of the ensemble, and/or new data sets, extracts the different signals with, again, a useful noise reduction. Both computer simulations and real positron annihilation data are used to illustrate these developments.