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Parameters of the model are estimated using quasi-likelihood estimation techniques.

The unknown parameters of EHS model are estimated by the parametric adaptive estimation law.

The parameters of the SBL model are estimated by the maximal posteriori probability estimation according to the Bayesian inference [19, 20].

In a final modeling step, the most important parameters of the model are estimated from the data obtained in this work.

The measurement model and structural model are estimated through partial least squares.

The parameters of the model are estimated by the LSM (least square method).

In the present work, all the parameters of a FOPTD model are estimated with adequate accuracy.

The unknown parameters in the model are estimated from experimental data.

The parameters of Brinson model are estimated experimentally using an extended Kalman filter (EKF).

Physical kinetic parameters and chemical kinetic parameters which arise from this model are estimated simultaneously.

The parameters of the model are estimated by fitting the model to the experimental data.