Lukas Ecker, Markus Schöberl,
"A data-driven approach for the identification of nonlinear state-dependent switched systems using expectation-maximization"
: 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), Seite(n) 361-366, 2022
Original Titel:
A data-driven approach for the identification of nonlinear state-dependent switched systems using expectation-maximization
Sprache des Titels:
Englisch
Original Buchtitel:
2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Original Kurzfassung:
A maximum likelihood-based identification algorithm for nonlinear state-dependent switched systems is presented. The data-based modeling of switched systems is in principle more demanding, since assignments of the sampled recordings to their originating subsystems are not given. The
resulting identification problem involves latent variables and is
therefore solved by an expectation-maximization algorithm. The estimated likelihoods are used to construct the switching condition by a decision tree learning algorithm. The performance of the proposed method is demonstrated by two examples.