We address the problem of classifying trajectories generated by dynamical systems. We consider the model-based approach, which is the classic approach in control theory, and (data-driven) Support Vector Machines, a popular method in the area of machine learning. The analysis points out connections between the two approaches and their relative merits. Examples are given to substantiate the analysis.

Classification for dynamical systems: model-based and data-driven approaches / Battistelli, Giorgio; Tesi, Pietro. - In: IEEE TRANSACTIONS ON AUTOMATIC CONTROL. - ISSN 0018-9286. - STAMPA. - 66:(2021), pp. 1741-1748. [10.1109/TAC.2020.2998975]

Classification for dynamical systems: model-based and data-driven approaches

Battistelli, Giorgio;Tesi, Pietro
2021

Abstract

We address the problem of classifying trajectories generated by dynamical systems. We consider the model-based approach, which is the classic approach in control theory, and (data-driven) Support Vector Machines, a popular method in the area of machine learning. The analysis points out connections between the two approaches and their relative merits. Examples are given to substantiate the analysis.
2021
66
1741
1748
Battistelli, Giorgio; Tesi, Pietro
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1218789
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