The bias compensated least squares approach for errors-in-variables model identification is examined in a new framework, where it is allowed to prefilter the observed input-output data prior to the estimation process. A statistical analysis of the estimation algorithm is presented. Subsequently, it is shown how these prefilters and the weighting matrix can be tuned in order to optimize the estimation accuracy. According to the numerical simulation results, the covariance matrix of the estimated parameter vector is very close to the Cramer-Rao lower bound for the estimation problem
AbstractThis paper reviews and extends some of the known results in the estimation in “errors-in-var...
none1noA new bias-compensated least-squares method for identifying finite impulse response (FIR) mod...
The problem of identifying dynamic errors-in-variables models is of fundamental interest in many are...
We present results about classes of prefilters that may result in similar model estimates by use of ...
We review some features related to the use of prefiltering data for identification. In addition to t...
This paper considers the problem of identifying linear systems, where the input is observed in white...
none1noThis paper proposes a bias-eliminating least-squares (BELS) approach for identifying linear d...
The bias-eliminating least squares (BELS) method is one of the consistent estimators for identifying...
This note considers the bias-eliminating least squares (BELS) method for identifying the errors-in-v...
Data prefiltering is often used in linear system identification to increase model accuracy in a spec...
The problem of dynamic errors-in-variable identification is studied in this paper. We investigate as...
AbstractFor ARX-like systems, this paper derives a bias compensation based recursive least squares i...
This paper considers the problem of dynamic errors-in-variables identification. Convergence properti...
grantor: University of TorontoDuring a process identification experiment, it often occurs ...
We study the problem of system identification for the errors-in-variables (EIV) model, based on nois...
AbstractThis paper reviews and extends some of the known results in the estimation in “errors-in-var...
none1noA new bias-compensated least-squares method for identifying finite impulse response (FIR) mod...
The problem of identifying dynamic errors-in-variables models is of fundamental interest in many are...
We present results about classes of prefilters that may result in similar model estimates by use of ...
We review some features related to the use of prefiltering data for identification. In addition to t...
This paper considers the problem of identifying linear systems, where the input is observed in white...
none1noThis paper proposes a bias-eliminating least-squares (BELS) approach for identifying linear d...
The bias-eliminating least squares (BELS) method is one of the consistent estimators for identifying...
This note considers the bias-eliminating least squares (BELS) method for identifying the errors-in-v...
Data prefiltering is often used in linear system identification to increase model accuracy in a spec...
The problem of dynamic errors-in-variable identification is studied in this paper. We investigate as...
AbstractFor ARX-like systems, this paper derives a bias compensation based recursive least squares i...
This paper considers the problem of dynamic errors-in-variables identification. Convergence properti...
grantor: University of TorontoDuring a process identification experiment, it often occurs ...
We study the problem of system identification for the errors-in-variables (EIV) model, based on nois...
AbstractThis paper reviews and extends some of the known results in the estimation in “errors-in-var...
none1noA new bias-compensated least-squares method for identifying finite impulse response (FIR) mod...
The problem of identifying dynamic errors-in-variables models is of fundamental interest in many are...