@inproceedings{1db6dcbffd8c4ef2bc60492e0a24c2be,

title = "A Continuous Representation Of Switching Linear Dynamic Systems For Accurate Tracking",

abstract = "We propose a method for tracking linear representations of a nonlinear dynamic system with time-varying parameters based on a continuous representation of its switching linear dynamic system (SLDS) model. Given approximate linear representations for a finite set of unknown intrinsic parameters of the dynamics, a combination of autoencoder-based dimensionality reduction and cubic curve-fitting are applied to learn the continuous manifold of dynamics embedded in the evolution operator. This representation enables a significant reduction of the squared Frobenius norm of error in maximum likelihood (ML) system identification relative to that of the original SLDS model. Numerical experiments also verify this result.",

keywords = "Dynamic systems, Koopman operator, Manifold learning, Online tracking, Switching linear dynamic system, Variational autoencoder",

author = "Parisa Karimi and Helmuth Naumer and Farzad Kamalabadi",

note = "Funding Information: This work was supported in part by the National Science Foundation under Grant 1936663. Publisher Copyright: {\textcopyright} 2023 IEEE.; 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 ; Conference date: 02-07-2023 Through 05-07-2023",

year = "2023",

doi = "10.1109/SSP53291.2023.10207936",

language = "English (US)",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

publisher = "IEEE Computer Society",

pages = "339--343",

booktitle = "Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023",

}