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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A hybrid principal-vector pruning framework refines Koopman subspace invariance with error bounds and rank-one update efficiency for lifted linear prediction.
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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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Subspace Pruning via Principal Vectors for Accurate Koopman-Based Approximations
A hybrid principal-vector pruning framework refines Koopman subspace invariance with error bounds and rank-one update efficiency for lifted linear prediction.