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This method uses the singular value decomposition (SVD) as the primary analysis tool and has shown some promise.
This algorithm uses the singular value decomposition technique to calculate the pseudo-inverse of the matrix needed to set the weights in a linear output layer, so as to find the least mean squared solution.
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Composition matrices for different hydrological conditions are analyzed by using the singular value decomposition technique.
The overdetermined system of linear equations is robustly solved using the singular value decomposition (SVD) method.
We then calculate the closest orthonormal basis to B using the singular value decomposition.
To estimate the parameters, and, we have used the singular value decomposition (SVD) approach.
Rank reduction is achieved by using the singular value decomposition (SVD).
The theoretical control properties of those schemes were obtained using the singular value decomposition technique in all frequency domain.
Five independent members of the alignment tensor were determined by using the singular value decomposition (SVD) method.
Using the singular value decomposition (SVD), the system matrix Ψ ˜ ˜ can be decomposed as: Ψ ˜ ˜ = UΣ V H, (4).
Most of these approaches are based on using the singular value decomposition along with matrix-form representations of Volterra kernels.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com