Existence of stationary points for pseudo-linear regression identification algorithms
PA Regalia, M Mboup, M Ashari - IEEE transactions on …, 1999 - ieeexplore.ieee.org
PA Regalia, M Mboup, M Ashari
IEEE transactions on automatic control, 1999•ieeexplore.ieee.orgThe authors prove the existence of a stable transfer function satisfying the nonlinear
equations characterizing an asymptotic stationary point, in undermodeled cases, for a class
of pseudo-linear regression algorithms, including Landau's algorithm, the Feintuch
algorithm, and (S) HARF. The proof applies to all degrees of undermodeling and assumes
only that the input power spectral density function is bounded and nonzero for all
frequencies, and that the compensation filter is strictly minimum phase. Some connections to …
equations characterizing an asymptotic stationary point, in undermodeled cases, for a class
of pseudo-linear regression algorithms, including Landau's algorithm, the Feintuch
algorithm, and (S) HARF. The proof applies to all degrees of undermodeling and assumes
only that the input power spectral density function is bounded and nonzero for all
frequencies, and that the compensation filter is strictly minimum phase. Some connections to …
The authors prove the existence of a stable transfer function satisfying the nonlinear equations characterizing an asymptotic stationary point, in undermodeled cases, for a class of pseudo-linear regression algorithms, including Landau's algorithm, the Feintuch algorithm, and (S)HARF. The proof applies to all degrees of undermodeling and assumes only that the input power spectral density function is bounded and nonzero for all frequencies, and that the compensation filter is strictly minimum phase. Some connections to previous stability analyses for reduced-order identification in this algorithm class are brought out.
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