Year: 2024
Type: preprint
Abstract: This paper proposes a general switching dynamical system model, and a custom majorization-minimization-based algorithm EM++ for identifying its parameters. For certain families of distributions, such as Gaussian distributions, this algorithm reduces to the well-known expectation-maximization method. We prove global convergence of the algorithm under suitable assumptions, thus addressing an important open issue in the switching system identification literature. The effectiveness of both the proposed model and algorithm is validated through extensive numerical experiments.
Source: arXiv (Cornell University)
Authors: Wang, Renzi, Bodard, Alexander, Schuurmans, Mathijs, Patrinos, Panagiotis
Institution: KU Leuven
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G434337789, Learning to optimize nonconvex problems: provably and efficiently, Taming Nonconvexity in Structured Low-Rank Optimization., Embedded learning and optimization for the next generation of smart industrial control systems, From model-based to data-driven: signal processing and control engineering for nonlinear systems
OpenAlex ID: W6910671575