A unified prediction-error-minimization framework separates deterministic dynamics from innovation noise and estimates both with L-BFGS-B and automatic differentiation, with consistency guarantees under classical assumptions.
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Efficient identification of linear, parameter-varying, and nonlinear systems with noise models
A unified prediction-error-minimization framework separates deterministic dynamics from innovation noise and estimates both with L-BFGS-B and automatic differentiation, with consistency guarantees under classical assumptions.