An incremental approach to online dynamic mode decomposition for time-varying systems with applications to EEG data modeling

DOI10.3934/jcd.2020009zbMath1450.37080arXiv1908.01047OpenAlexW3043552674MaRDI QIDQ2194421

Mustaffa Alfatlawi, Vaibhav Srivastava

Publication date: 26 August 2020

Published in: Journal of Computational Dynamics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.01047

zbMATH Keywords

time-varying systemsdynamic mode decompositionincremental algorithmsdata-driven dynamics

Mathematics Subject Classification ID

Approximation methods and numerical treatment of dynamical systems (37M99) General topics in computing methodologies (68U01)

Related Items (3)

Detection and prediction of equilibrium states in kinetic plasma simulations via mode tracking using reduced-order dynamic mode decomposition ⋮ Modern Koopman Theory for Dynamical Systems ⋮ Dynamic mode decomposition of deformation fields in elastic and elastic-plastic solids

Uses Software

• EEGLAB
• odmd
• libROM

Cites Work

• A data-driven approximation of the koopman operator: extending dynamic mode decomposition
• Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
• Comparison of systems with complex behavior
• Fast low-rank modifications of the thin singular value decomposition
• Spectral properties of dynamical systems, model reduction and decompositions
• On dynamic mode decomposition: theory and applications
• Dynamic Mode Decomposition with Control
• Dynamic mode decomposition of numerical and experimental data
• Spectral analysis of nonlinear flows
• Hamiltonian Systems and Transformation in Hilbert Space
• Koopman Operator Family Spectrum for Nonautonomous Systems
• Analysis of Fluid Flows via Spectral Properties of the Koopman Operator
• Identification of Dynamic Systems
• Adaptive, locally linear models of complex dynamics
• Online Dynamic Mode Decomposition for Time-Varying Systems
• Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix
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