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A Leontief matrix of input coefficients.
The method is based on analyzing the eigenvalues of the covariance matrix of input signals.
Hence, the matrix of input regarding the MLP for training consists of 512 rows and 260 columns.
On the other hand, subspace-based techniques produce the spatial spectrum by using Eigen-decomposition of the covariance matrix of input signals, from which AOA is estimated.
In Equation 2, I demonstrates the target function (amount of rotation in normalization space), c represents column matrix of input data in normalization space, w shows the amount of weight, and b exhibits the amount of bias.
If the data in the input vectors are not orthogonal, then the activation function looks like this begin{aligned} psi _2 (u^{j})=e^{frac{- x-w^{j})^{T}Sigma ^{-1}(x-w^{j})}{2}}, end{aligned} (8)where (sum ^{-1}) is the inverse of the variance–covariance matrix of input data.
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The authors, in particular, have theoretically demonstrated that the exact statistical properties (spectral moments) of the PSD functions matrix of stress tensor of the model are obtainable only from PSD functions matrix of its modal coordinates and from PSD functions matrix of inputs.
As shown in the GRNN method section, y i denotes the benchmark NCI of the training set, X is the transposed matrices of input neurons (four selected descriptors) and X i is the input neuron corresponding to the ith pattern neuron, all of which are known inputs.
Furthermore, it is unclear if or why minimizing the parsimony score of a matrix representation of input trees is a good optimality criterion or should produce accurate supertrees.
({tilde{mathbf{A}}}_{text{MP}}) stands for the matrix of the input coefficients of materials to the production of products, and ({tilde{mathbf{A}}}_{text{PP}}) is for the input of products to the production of products.
Figure 2 visualises the EVD-based PCA performed on the correlation matrix of the input data.
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