Quantized EDMD predictor estimates converge, as data grows, to a regularized least-squares solution of the unquantized problem, with finite-data errors of order ϵ.
Optimizing neural networks via koopman operator theory,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.SY 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Koopman Meets Limited Bandwidth: Effect of Quantization on Data-Driven Linear Prediction and Control of Nonlinear Systems
Quantized EDMD predictor estimates converge, as data grows, to a regularized least-squares solution of the unquantized problem, with finite-data errors of order ϵ.