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**Harvard**

Ngia, L. (2001) *Separable nonlinear least-squares methods for efficient off-line and on-line modeling of systems using Kautz and Laguerre filters*.

** BibTeX **

@article{

Ngia2001,

author={Ngia, Lester S.H.},

title={Separable nonlinear least-squares methods for efficient off-line and on-line modeling of systems using Kautz and Laguerre filters},

journal={IEEE transactions on circuits and systems - 2, Analog and digital signal processing},

issn={1057-7130},

volume={48},

issue={6},

pages={562-579},

abstract={Kautz and Laguerre filters are effective linear regression models that can describe accurately an unknown linear system with a fewer parameters than finite-impulse response (FIR) filters. This is achieved by expanding the transfer functions of the Kautz and Laguerre filters around some a priori knowledge, concerning the dominating time constants or resonant modes of the system to be identified. When the estimation of these filters is based on a minimization of the least-squares error criterion, the minimization problem becomes separable with respect to the linear coefficients. Therefore, the original unseparated problem can be reduced to a separated problem in only the nonlinear poles, which is numerically better conditioned than the original unseparated one. This paper proposed batch and recursive algorithms that are derived using this separable nonlinear least-squares method, for the estimation of the coefficients and poles of Kautz and Laguerre filters. They have similar computational loads, but better convergence properties than their corresponding algorithms that solve the unseparated problem. The performance of the suggested algorithms is compared to alternative batch and recursive algorithms in some system identification examples. Generally, it is shown that the proposed batch and recursive algorithms have better convergence properties than the alternatives. },

year={2001},

keywords={echo cancelation; Gauss-Newton algorithm; Kautz filter; Laguerre filter; off-line estimation; on-line estimation; separable nonlinear least-squares; steepest-descent algorithm},

}

** RefWorks **

RT Journal Article

SR Electronic

ID 14965

A1 Ngia, Lester S.H.

T1 Separable nonlinear least-squares methods for efficient off-line and on-line modeling of systems using Kautz and Laguerre filters

YR 2001

JF IEEE transactions on circuits and systems - 2, Analog and digital signal processing

SN 1057-7130

VO 48

IS 6

SP 562

OP 579

AB Kautz and Laguerre filters are effective linear regression models that can describe accurately an unknown linear system with a fewer parameters than finite-impulse response (FIR) filters. This is achieved by expanding the transfer functions of the Kautz and Laguerre filters around some a priori knowledge, concerning the dominating time constants or resonant modes of the system to be identified. When the estimation of these filters is based on a minimization of the least-squares error criterion, the minimization problem becomes separable with respect to the linear coefficients. Therefore, the original unseparated problem can be reduced to a separated problem in only the nonlinear poles, which is numerically better conditioned than the original unseparated one. This paper proposed batch and recursive algorithms that are derived using this separable nonlinear least-squares method, for the estimation of the coefficients and poles of Kautz and Laguerre filters. They have similar computational loads, but better convergence properties than their corresponding algorithms that solve the unseparated problem. The performance of the suggested algorithms is compared to alternative batch and recursive algorithms in some system identification examples. Generally, it is shown that the proposed batch and recursive algorithms have better convergence properties than the alternatives.

LA eng

DO 10.1109/82.943327

LK http://dx.doi.org/10.1109/82.943327

OL 30