43rd IEEE Conference on Decision and Control (CDC…

1 April 2005

We prove that if a component of the response signal of a controllable linear time-invariant system is persistently exciting of sufficiently high order, then the windows of the signal span the full… Expand

It is explained how special structure of the weight matrix and the data matrix can be exploited for efficient cost function and first derivative computation that allows to obtain computationally efficient solution methods.Expand

Data Approximation by Low-complexity Models details the theory, algorithms, and applications of structured low-rank approximation and describes the applications including: system and control theory; signal processing; computer algebra for approximate factorization and common divisor computation; computer vision for image deblurring and segmentation.Expand

This work outlines applications in system theory (approximate realization, model reduction, output error, and errors-in-variables identification), signal processing, signal processing (harmonic retrieval, sum-of-damped exponentials, and finite impulse response modeling), and computer algebra (Approximate common divisor).Expand

An approach for computing a linear quadratic tracking control signal that circumvents the identification step is presented and the results are derived assuming exact data and the simulated response or control input is constructed off-line.Expand

Modified total least-squares problem is formulated so that it provides a consistent estimator, i.e., the estimate [email protected]^ converges to the true value X"0 as the number of measurements increases.Expand

A review of existing modeling techniques with particular emphasis on their limitations is undertaken and a Hammerstein structure is selected and a suitable identification procedure and set of excitation inputs are developed to address these short-comings.Expand

An overview of the progress of a modeling technique known as Total Least Squares in computational mathematics and engineering, and as Errors-InVariables (EIV) modeling or orthogonal regression in the statistical community is presented.Expand

This paper considers low-rank approximation of affinely structured matrices with missing elements, a singular linear least-norm problem, based on reformulation of the problem as inner and outer optimization.Expand

It is shown that the computations can be performed on Hankel matrices of the input-output data of various dimensions and what is the optimal in terms of minimal identifiability condition partition of the data into ''past'' and ''future''.Expand