Parameter Estimation for Nonlinear Continuous- Time State-space Models from Sampled Data
نویسنده
چکیده
The problem of parameter estimation for nonlinear state-space models is addressed. Two approaches to this problem are presented: (1) the state-augmentation approach, which consists of including the unknown system parameters in the state vector and estimating them through a state estimator, and (2) the prediction-error approach, which consists of tuning a predictor such that it will give optimal predictions and then recovering the system parameters from this optimum predictor.
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