Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Information Technologies and Control

The Journal of Institute of Information and Communication Technologies of Bulgarian Academy of Sciences

4 Issues per year

Open Access
Online
ISSN
1312-2622
See all formats and pricing
More options …

General Forms of a Class of Multivariable Regression Models

A. Efremov
Published Online: 2014-10-02 | DOI: https://doi.org/10.2478/itc-2013-0009

Abstract

There are two possible general forms of multiple input multiple output (MIMO) regression models, which are either linear with respect to their parameters or non-linear, but in order to estimate their parameters, at a certain stage it could be assumed that they are linear. This is in fact the basic assumption behind the linear approach for parameters estimation. There are two possible representations of a MIMO model, which at a certain level could be fictitiously presented as linear functions of its parameters. One representation is when the parameters are collected in a matrix and hence, the regressors are in a vector. The other possible case is the parameters to be in a vector, but the regressors at a given instant to be placed in a matrix. Both types of representations are considered in the paper. Their advantages and disadvantages are summarized and their applicability within the whole experimental modelling process is also discussed.

Keywords: MIMO model; linear parameterization; parameter matrix form; parameter vector form; stepwise regression

References

  • 1. Efremov, A. Multivariate Time-Varying System Identification at Incomplete Information. Technical University of Sofia, Faculty of Automatics, Ph.D. 2008.Google Scholar

  • 2. Efremov, A. System Identification Based on Stepwise Regression for Dynamic Market Representation. International Conference on Data Mining and Knowledge Engineering, Rome, Italy, 28–30 April 2010, 64, 2010, No. 2, 132-137.Google Scholar

  • 3. Efremov, A. Multivariable System Identification. Monograph, Dar – RH, ISBN 978-954-9489-34-7, 2013.Google Scholar

  • 4. Faraway, J. Practical Regression and ANOVA Using R. http:// cran.r-project.org/doc/contrib/Faraway-PRA.pdf, 2002.Google Scholar

  • 5. Van den Hof, P. M. J. Model Sets and Parameterizations for Identification of Multivariable Equation Error Models. – Automatica, 30 (3), 1994, 433-446.Google Scholar

  • 6. Vuchkov, I. Identification. Sofia, IK Jurapel, 1996.Google Scholar

About the article

Received: 2013-12-18

Published Online: 2014-10-02


Citation Information: Information Technologies and Control, Volume 11, Issue 2, Pages 22–28, ISSN (Online) 1312-2622, DOI: https://doi.org/10.2478/itc-2013-0009.

Export Citation

© 2013 A. Efremov. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Comments (0)

Please log in or register to comment.
Log in