Defines a positivity certificate for finite-sample identifiability of the control-channel block in Koopman EDMDc and derives closed-loop statistical bounds under behavior policies.
Dynamic mode decomposition with control
5 Pith papers cite this work. Polarity classification is still indexing.
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Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
The relative root-mean-square error of finite-dimensional Koopman Control Family predictors is strictly upper-bounded by the square root of the largest eigenvalue of the newly defined control forward-backward consistency matrix.
RC-Koopman uses reservoir computing as a stateful Koopman dictionary with spectral radius controlling temporal memory to achieve accurate and stable identification of nonlinear systems.
A framework identifies homogeneous polynomial dynamical systems from data by directly learning low-rank tensor factors via alternating least-squares on tensor train, hierarchical Tucker, and canonical polyadic decompositions.
citing papers explorer
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Control-Channel Informativity for Koopman EDMDc under Behavior-Policy Data
Defines a positivity certificate for finite-sample identifiability of the control-channel block in Koopman EDMDc and derives closed-loop statistical bounds under behavior policies.
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Limitations of LTI Koopman Modeling for Nonlinear Control Systems
Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
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Control Forward-Backward Consistency: Quantifying the Accuracy of Koopman Control Family Models
The relative root-mean-square error of finite-dimensional Koopman Control Family predictors is strictly upper-bounded by the square root of the largest eigenvalue of the newly defined control forward-backward consistency matrix.
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Koopman Identification of Nonlinear Systems via Reservoir Liftings
RC-Koopman uses reservoir computing as a stateful Koopman dictionary with spectral radius controlling temporal memory to achieve accurate and stable identification of nonlinear systems.
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Data-Driven Tensor Decomposition Identification of Homogeneous Polynomial Dynamical Systems
A framework identifies homogeneous polynomial dynamical systems from data by directly learning low-rank tensor factors via alternating least-squares on tensor train, hierarchical Tucker, and canonical polyadic decompositions.