1976Electronics LettersRequires access

Multivariable parameter estimation using a 2-step least-squares estimator

B.A. Abaza

Open publisher page 9 citations

Abstract

A 2-step least-squares technique is extended to deal with the problem of estimating the parameters of the transfer-function matrix of a multivariable linear discrete system. The method does not require assumptions such as common dynamics and a single noise source. Results on its performance are presented.

About this research paper

What this paper is about

A 2-step least-squares technique is extended to deal with the problem of estimating the parameters of the transfer-function matrix of a multivariable linear discrete system. The method does not require assumptions such as common dynamics and a single noise source. Results on its performance are presented.

Why it matters

OpenAlex reports 9 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

A 2-step least-squares technique is extended to deal with the problem of estimating the parameters of the transfer-function matrix of a multivariable linear discrete system. The method does not require assumptions such as common dynamics and a single noise source. Results on its performance are presented.

Key concepts: Multivariable calculus, Estimator, Transfer function, Mathematics, Control theory (sociology), Least-squares function approximation, Noise (video), Estimation theory

Related papers

Back to paper searchBrowse research topicsOriginal source
Multivariable parameter estimation using a 2-step least-squares estimator — Research Paper | ScholarLens