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Theory and Practice of Recursive Identification
Methods of recursive identification deal with the problem of building mathematical models of signals and systems on-line, at the same time as data is being collected. Expand
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Instrumental variable methods for system identification
This paper gives a tutorial overview of instrumental variable methods. Comparisons are made to the least-squares method. An analysis including consistency and asymptotic distribution of the parameterExpand
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Errors-in-variables methods in system identification
The paper gives a survey of errors-in-variables methods in system identification. Expand
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Discrete-Time Stochastic Systems: Estimation and Control
From the Publisher: The aim of this text is to give a comprehensive introduction to the field of stochastic dynamic systems, their estimation and control, including the provision of completeExpand
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Maximum likelihood estimation of the parameters of multiple sinusoids from noisy measurements
We derive a simplified maximum-likelihood Gauss-Newton algorithm which provides asymptotically efficient estimates of these parameters of sinusoidal signals in noise. Expand
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Bias correction in least-squares identification
Abstract The least-squares method in system identification leads generally to biased parameter estimates. A conceptually simple modification is to estimate the bias and to compute compensatedExpand
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Identifiability conditions for linear multivariable systems operating under feedback
The possibility of estimating parameters of a dynamic system when it is operating in closed loop is examined. Expand
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Statistical analysis of MUSIC and subspace rotation estimates of sinusoidal frequencies
Analysis of the large-sample second-order properties of multiple signal classification (MUSIC) and subspace rotation (SUR) methods, such as ESPRIT, for sinusoidal frequency estimation. Expand
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Identification of stochastic linear systems in presence of input noise
Identify systems from noisy data by treating them as outputs of a multivariable stochastic system . Expand
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On reparametrization of loss functions used in estimation and the invariance principle
Abstract The problem addressed in this note concerns the relationship between the minimizers of a given loss function parametrized in two different ways. The so-called “invariance principle” (IP)Expand
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