Obtain the measured input/output data.

Estimate a first-order plus dead time process model.

Compare the model with the data.

Plot the model residuals.

The figure shows that the residuals are correlated. To account for that, add a first order ARMA disturbance component to the process model.

Compare the models.

Plot the model residuals.

The residues of `sysP1D_noise`

are uncorrelated.

Use regularization to estimate parameters of an over-parameterized process model.

Assume that gain is known with a higher degree of confidence than other model parameters.

Load data.

Estimate an unregularized process model.

Estimate a regularized process model.

Compare the model outputs with data.

Regularization helps steer the estimation process towards the correct parameter values.

Estimate a process model after specifying initial guesses for parameter values and bounding them.

Obtain input/output data.

Specify the parameters of the estimation initialization model.

Specify the estimation options.

Estimate the process model.

Since the `'Display'`

option is specified as `'full'`

, the estimation progress is displayed in a separate **Plant Identification Progress** window.

Compare the data to the estimated model.

Obtain input/output data.

`data`

is a data set with 2 inputs and 2 outputs. The first input affects only the first output. Similarly, the second input affects only the second output.

In the estimated process model, the cross terms, modeling the effect of the first input on the second output and vice versa, should be negligible. If higher orders are assigned to those dynamics, their estimations show a high level of uncertainty.

Estimate the process model.

The `type`

variable denotes a model with complex-conugate pair of poles, a zero, and a delay.

To evaluate the uncertainties, plot the frequency response.