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(ii) The least-squares solutions of the equation has the same formulation as (16) and the least-squares solution with the minimum norm is (x=A^b). .
The least-squares solutions of the equation has the same formulation as (16) and the least-squares solution with the minimum norm is (x=A^b).
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In the feature extraction stage, three eigenvector methods such as the Pisarenko, music, and minimum norm were used to obtain the PSD values of the EEG signals that were employed as the input of the multiclass SVMs.
For the real matrix equation (1), in [16], least-squares solutions with the minimum-norm were obtained by using generalized singular value decomposition and canonical correlation decomposition of matrices (CCD).
An extended underdetermined design matrix is then formed, and the estimator of the extended parameters with minimum l1 norm is computed.
The linear combination of the basis vectors that fulfills the parabolic condition and has a minimum residual norm is determined using Lagrange multipliers.
The angular effect described above can also be observed in the noiseless case when the LP model order, in which case the "extraneous" PEF zeros tend to be uniformly distributed around the unit circle if a minimum norm constraint is incorporated in the LP criterion [45].
Optimal design solutions for filters having minimum H2 norm are derived and evaluated.
In this case, S b has a unique element with minimum norm; that is, there exists a unique point x† ∈ S b satisfying x † 2 = min x 2 : x ∈ S b. (3.17).
they are orthogonal with regard to the scalar product: (5) (6). the square norm is minimum.
Then one can uniquely separate into two parts ad and na, or ge and ng, such that 1) they are orthogonal with regard to the scalar product: (5) (6) 2) the square norm is minimum. .
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