We investigate motor and load speed feedback schemes by utilizing the singular perturbation method.
For a set of given mode-isolated signals, the un-damped mode shapes are extracted utilizing the singular value decomposition of the output energy correlation matrix with respect to sensor locations.
This technique utilizes the singular value decomposition (SVD), allowing regularization on the spectral Jacobian matrix to be applied without altering the underlying spectral prior information.
It utilizes the singular value decomposition of the normalized predictors, and allows bigger deviation from zero to the regression parameters of the orthonormalized predictors which are known to explain the original predictor variability most, i.e., have biggest singular values.
Recently, many scholars proposed several kinds of biological dynamic systems [1, 3 6] by utilizing the theory of singular system, which studies the dynamic system with capture factors.
Implicit numerical integration methods that account for the higher order derivative information are utilized for solving the singular set of differential equations.
The GAP elliptical seriation [ 22] utilizing the ellipse structure from a singular value decomposition of a converged correlation coefficient matrix usually identifies permuted matrix with a near Robinson form.
In this paper, utilizing the framework of the theory of singular difference equations, we give some necessary and sufficient conditions for the ϕ 0 -stability of a trivial solution of singular difference equations via cone-valued Lyapunov functions.
Reduced-order state space models suitable for process control studies are constructed by utilizing the methods of orthogonal collocation and singular perturbation.
The particular factorization permits us to utilize the product SVD [51, 52] and expresses the singular value decomposition of the product according to the expression D L=U(G 1 G 2 V ∗, where the singular values of the matrix product are given by the product of the singular values of the corresponding matrices.
