A hybrid iterative-sequential method identifies linear DAE systems from errors-in-variables data by partial lagged-data stacking and iterative diagonal error-covariance estimation.
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2026 2verdicts
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RL agent learns optimal excitation signals for Quanser Aero 2 parameter identification, achieving competitive accuracy on three parameters with 0.75% safety violations and outperforming classical baselines.
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DISPCA : A hybrid iterative-sequential approach for the identification of errors-in-variables model of linear DAE systems
A hybrid iterative-sequential method identifies linear DAE systems from errors-in-variables data by partial lagged-data stacking and iterative diagonal error-covariance estimation.
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Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
RL agent learns optimal excitation signals for Quanser Aero 2 parameter identification, achieving competitive accuracy on three parameters with 0.75% safety violations and outperforming classical baselines.