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A technique for the identification of linear systems
Kenneth Steiglitz,L. McBride +1 more
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In this paper, an iterative technique is proposed to identify a linear system from samples of its input and output in the presence of noise by minimizing the mean-square error between system and model outputs.Abstract:
An iterative technique is proposed to identify a linear system from samples of its input and output in the presence of noise by minimizing the mean-square error between system and model outputs. The model chosen has a transfer function which is a ratio of polynomials in z-1. Although the regression equations for the optimal set of coefficients are highly nonlinear and intractable, it is shown that the problem can be reduced to the repeated solution of a related linear problem. Computer simulation of a number of typical discrete systems is used to demonstrate the considerable improvement over the Kalman estimate which can be obtained in a few iterations. The procedure is found to be effective at signal-to-noise ratios less than unity, and with as few as 200 samples of the input and output records.read more
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Two decades of array signal processing research: the parametric approach
Hamid Krim,Mats Viberg +1 more
TL;DR: The article consists of background material and of the basic problem formulation, and introduces spectral-based algorithmic solutions to the signal parameter estimation problem and contrast these suboptimal solutions to parametric methods.
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System identification-A survey
Karl Johan Åström,Pieter Eykhoff +1 more
TL;DR: The survey explains the least squares method and several of its variants which may solve the problem of correlated residuals, viz. repeated and generalized least squares, maximum likelihood method, instrumental variable method, tally principle.
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An iterative method for the identification of nonlinear systems using a Hammerstein model
Kumpati S. Narendra,P. Gallman +1 more
TL;DR: In this article, an iterative method is proposed for the identification of nonlinear systems from samples of inputs and outputs in the presence of noise, which consists of a no-memory gain (of an assumed polynomial form) followed by a linear discrete system.
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All-pole modeling of degraded speech
Jae Lim,Alan V. Oppenheim +1 more
TL;DR: This paper considers the estimation of speech parameters in an all-pole model when the speech has been degraded by additive background noise and develops a procedure based on maximum a posteriori (MAP) estimation techniques which is related to linear prediction analysis of speech.
Book
Identification of Dynamic Systems: An Introduction with Applications
Rolf Isermann,Marco Münchhof +1 more
TL;DR: This book treats the determination of dynamic models based on measurements taken at the process, known as system identification or process identification, and covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation and subspace methods.
References
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Journal ArticleDOI
Estimation of a system pulse transfer function in the presence of noise
TL;DR: In this paper, a method of Koopmans is applied to obtain generalized least squares estimates which are also maximum likelihood estimates when the noise is white and Gaussian, and the appropriate modifications for nonwhite noise are described.
Journal ArticleDOI
Optimum Estimation of Impulse Response in the Presence of Noise
TL;DR: In this paper, the impulse response of a linear system from records of its input and output during a limited interval of time when the system output is obscured by additive random noise is estimated.
Journal ArticleDOI
The identification of linear systems
TL;DR: This paper presents a procedure for identifying a linear lumped-parameter time-invariant (at least during the measurement period) single-input single-output system from its response to a step excitation.