The behavioral toolbox

Ivan Markovsky
Proceedings of the 6th Annual Learning for Dynamics & Control Conference, PMLR 242:130-141, 2024.

Abstract

The Behavioral Toolbox is a collection of Matlab functions for modeling, analysis, and design of dynamical systems using the behavioral approach to systems theory and control. It implements newly emerged direct data-driven methods as well as classical parametric representations of linear time-invariant systems. At the core of the toolbox is a nonparameteric representation of the finite-horizon behavior by an orthonormal basis. The current version has education and research goals and isn’t intended for handling “big data”. The paper presents five problems — checking systems equality, interconnection of systems, errors-in-variables least-squares smoothing, missing input estimation, and data-driven forecasting — and describes their solution by the methods in the toolbox.

Cite this Paper

BibTeX
@InProceedings{pmlr-v242-markovsky24a, title = {The behavioral toolbox}, author = {Markovsky, Ivan}, booktitle = {Proceedings of the 6th Annual Learning for Dynamics & Control Conference}, pages = {130--141}, year = {2024}, editor = {Abate, Alessandro and Cannon, Mark and Margellos, Kostas and Papachristodoulou, Antonis}, volume = {242}, series = {Proceedings of Machine Learning Research}, month = {15--17 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v242/markovsky24a/markovsky24a.pdf}, url = {https://proceedings.mlr.press/v242/markovsky24a.html}, abstract = {The Behavioral Toolbox is a collection of Matlab functions for modeling, analysis, and design of dynamical systems using the behavioral approach to systems theory and control. It implements newly emerged direct data-driven methods as well as classical parametric representations of linear time-invariant systems. At the core of the toolbox is a nonparameteric representation of the finite-horizon behavior by an orthonormal basis. The current version has education and research goals and isn’t intended for handling “big data”. The paper presents five problems — checking systems equality, interconnection of systems, errors-in-variables least-squares smoothing, missing input estimation, and data-driven forecasting — and describes their solution by the methods in the toolbox.} }
Endnote
%0 Conference Paper %T The behavioral toolbox %A Ivan Markovsky %B Proceedings of the 6th Annual Learning for Dynamics & Control Conference %C Proceedings of Machine Learning Research %D 2024 %E Alessandro Abate %E Mark Cannon %E Kostas Margellos %E Antonis Papachristodoulou %F pmlr-v242-markovsky24a %I PMLR %P 130--141 %U https://proceedings.mlr.press/v242/markovsky24a.html %V 242 %X The Behavioral Toolbox is a collection of Matlab functions for modeling, analysis, and design of dynamical systems using the behavioral approach to systems theory and control. It implements newly emerged direct data-driven methods as well as classical parametric representations of linear time-invariant systems. At the core of the toolbox is a nonparameteric representation of the finite-horizon behavior by an orthonormal basis. The current version has education and research goals and isn’t intended for handling “big data”. The paper presents five problems — checking systems equality, interconnection of systems, errors-in-variables least-squares smoothing, missing input estimation, and data-driven forecasting — and describes their solution by the methods in the toolbox.
APA
Markovsky, I.. (2024). The behavioral toolbox. Proceedings of the 6th Annual Learning for Dynamics & Control Conference, in Proceedings of Machine Learning Research 242:130-141 Available from https://proceedings.mlr.press/v242/markovsky24a.html.

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