Before you begin estimating the parameters, you must have configured the estimation data, selected parameters, and specified estimation options, as described in Specify Estimation Data, Specify Parameters for Estimation, and Estimation Options, respectively.
To start the estimation, in the Parameter Estimation tool, on the Parameter Estimation tab, click the Estimate button .
When starting the estimation, a progress window displays. At the end of the estimation, the Estimation Progress Report window should resemble the following:
The estimation results are saved in EstimatedParams in the Results list on the Browse Data pane.
Right-click EstimatedParams and select Open... from the menu. The window looks like the following figure.
The EstimatedParams includes the values of the parameters, the cost function value, and information about the stopping criteria for the estimation. The optimization stops because the successive function values are less than the specified value 1e-3.
The Estimation Progress Report includes the change in the cost function in the column titled NewData(Minimize). To see a plot of the change in the cost function during estimation, add the cost function plot by clicking the Add Plot button on the Parameter Estimation tab and selecting Estimation Cost from the list. After the estimation process completes, the cost function minimization plot appears as shown in the following figure.
Usually, a lower cost function value indicates a successful estimation, meaning that the experimental data matches the model simulation with the estimated parameters. If the optimization went well, you should see your cost function converge on a minimum value. The lower the cost, the more successful is the estimation.
For information on types of problems you may encounter using optimization solvers, see the following topics in the Optimization Toolbox™ documentation:
The estimated parameters graph shows the change in the estimated value of the parameters by iteration.
The values of the parameters are recorded with the estimated values.
The values of the estimated parameters are also updated in the MATLAB® workspace.
You can also examine the measured versus simulated data plot to see how closely the simulated data matches the measured estimation data. The next figure shows the measured versus simulated data plot generated by running the estimation of the engine_idle_speed model (for engine_idle_speed model, see Create Experiment). Now, the simulated values match the measured output signal better.