# Estimate Process Model

Estimate continuous-time process model for single-input, single-output (SISO) system in either time or frequency domain in the Live Editor

*Since R2019b*

## Description

The **Estimate Process Model** task lets you
interactively estimate and validate a process model for SISO systems. You can define and vary
the model structure and specify optional parameters, such as initial condition handling and
search methods. The task automatically generates MATLAB^{®} code for your live script. For more information about Live Editor tasks
generally, see Add Interactive Tasks to a Live Script.

Process models are simple continuous-time transfer functions that describe the linear system dynamics. Process model elements include static gain, time constants, time delays, integrator, and process zero.

Process models are popular for describing system dynamics in numerous industries and are applicable to various production environments. The advantages of these models are that they are simple, they support transport delay estimation, and the model coefficients are easy to interpret as poles and zeros. For more information about process model estimation, see What Is a Process Model?

The **Estimate Process Model** task is independent of the
more general **System Identification** app. Use the **System
Identification** app when you want to compute and compare estimates for multiple model
structures.

To get started, load experiment data that contains input and output data into your MATLAB workspace and then import that data into the task. Then select a model structure to estimate. The task gives you controls and plots that help you experiment with different model structures and compare how well the output of each model fits the measurements.

## Open the Task

To add the **Estimate Process Model** task to a live script
in the MATLAB Editor:

On the

**Live Editor**tab, select**Task > Estimate Process Model**.In a code block in your script, type a relevant keyword, such as

`process`

or`estimate`

. Select`Estimate Process Model`

from the suggested command completions.

## Examples

### Estimate Process Model with Live Editor Task

Use the **Estimate Process Model** Live Editor Task to estimate a state-space model and compare the model output to the measurement data.

Open this example to see a preconfigured script containing the task.

**Set Up Data**

Load the measurement data `iddata1`

into your MATLAB workspace.

load iddata1 z1 z1

z1 = Time domain data set with 300 samples. Sample time: 0.1 seconds Outputs Unit (if specified) y1 Inputs Unit (if specified) u1

**Import Data into the Task**

In the **Select data** section, set **Data Type** to `Data Object`

and set **Estimation Object** to `z1`

.

The data object contains the input and output variable names as well as the sample time, so you do not have to specify them.

**Estimate the Model Using Default Settings**

Examine the model structure and optional parameters.

In the **Specify model structure **section, the default option is `One Pole`

with no delay, zero, or integrator. Equations below the parameters in this section display the specified structure.

In the **Specify estimation initialization** section, initialization parameters matching the parameters in your model structure allow you to set starting points for estimation. If you select **Fix**, the parameter remains fixed to the value you specify. For this example, do not specify initialization. The task then uses default values for starting points.

In the **Specify optional parameters** section, the default options for process estimation are set.

Execute the task from the **Live Editor** tab using **Run**. A plot displays the estimation data, the estimated model output, and the fit percentage.

**Experiment with Parameter Settings**

Experiment with the parameter settings and see how they influence the fit.

For instance, add delay to the `One Pole`

structure and run the task.

The estimation fit improves, although the fit percentage is still below 50%.

Try a different model structure. In **Specify model structure**, select `Underdamped Pair`

with no delay and run the task.

The fit results improve significantly.

**Generate Code**

To display the code that the task generates, click at the bottom of the parameter section. The code that you see reflects the current parameter configuration of the task.

### Use Validation Data Set to Validate Estimated Model

Use separate estimation and validation data so that you can validate the estimated process model.

Open this example to see a preconfigured script containing the task.

**Set Up Data**

Load the measurement data `iddata1`

into your MATLAB workspace and examine its contents.

load iddata1 z1 z1

z1 = Time domain data set with 300 samples. Sample time: 0.1 seconds Outputs Unit (if specified) y1 Inputs Unit (if specified) u1

Extract the input and output measurements.

u = z1.u; y = z1.y;

Split the data into two sets, with one half for estimation and one half for validation. The original data set has 300 samples, so each new data set has 150 samples.

u_est = u(1:150); u_val = u(151:300); y_est = y(1:150); y_val = y(151:300);

**Import Data into Task**

In the **Select data** section, set **Data Type** to Time. Set the sample time to `0.1`

seconds, which is the sample time in the original `iddata`

object `z1`

. Select the appropriate data sets for estimation and validation.

**Estimate and Validate the Model**

The example Estimate Process Model with Live Editor Task achieves the best results using the model structure `Underdamped Pair`

. Choose the same option for this example.

Execute the task from the **Live Editor** tab using **Run**. Executing the task creates two plots.The first plot shows the estimation results and the second plot shows the validation results.

The fit to the estimation data is somewhat worse than in Estimate Process Model with Live Editor Task. Estimation in the current example has only half the data with which to estimate the model. The fit to the validation data, which represents the goodness of the model more generally, is better than the fit to the estimation data.

## Parameters

**Select Data**

`Data Type`

— Data type for input and output data

`Time`

(default) | `Frequency`

| `Data Object`

The task accepts single-channel numeric measurement values that are uniformly
sampled in time. Data can be packaged as numeric arrays (`Time`

or
`Frequency`

type) or in a data object, such as an `iddata`

or `idfrd`

object.

The data type you choose determines the additional parameters you must specify.

`Time`

— Specify**Sample Time**and**Start Time**in the time units that you select.`Frequency`

— Specify**Frequency**by selecting the variable name of a frequency vector in your MATLAB workspace. Specify the units for this frequency vector. Specify**Sample Time**in seconds.`Data Object`

— Specify no additional parameters, because the data object already contains information on time or frequency sampling.

`Estimation Input and Estimation Output`

— Variable names of input and output data for estimation

valid variable names

Select the input and output variable names from the MATLAB workspace choices. Use these parameters when **Data
Type** is `Time`

or
`Frequency`

.

`Estimation Object`

— Variable name of data object containing input and output data to be used for estimation

valid variable name

Select the data object variable name from the MATLAB workspace choices. Use this parameter when **Data Type**
is `Data Object`

.

`Validation Input (u) and Validation Output (y)`

— Variable names of input and output data to be used for validation

valid variable names

Select the input and output variable names, or the data object name, from the
workspace choices. Use these parameters when **Data Type** is
`Time`

or `Frequency`

. Specifying
validation data is optional but recommended.

`Validation Object`

— Variable name of data object containing input and output data for validation

valid variable name

Select the data object variable name from the MATLAB workspace choices. Use this parameter when **Data Type**
is `Data Object`

. Specifying validation data is optional but
recommended.

**Specify Model Structure**

`Structure`

— Zeros and poles in model

`One Pole`

(default) | `Two Real Poles`

| `Underdamped Pair`

| `Underdamped Pair + Real Pole`

The task allows you to specify one of four basic structures. These structures range from a simple first-order process to a more dynamic second-order or third-order process with complex conjugate (underdamped) poles.

`One Pole`

`Two Real Poles`

`Underdamped Pair`

`Underdamped Pair + Real Pole`

`Delay`

— Include transport delay

off (default) | on

Include transport delay, or input-to-output delay, of one sample. The transport delay is also known as dead time.

`Zero`

— Include process zero

off (default) | on

Include a process zero in the numerator.

`Integrator`

— Include integrator

off (default) | on

Include an integrator, represented by an additional 1/ *s* term.
Including an integrator creates a self-regulating process.

**Specify Estimation Initialization**

`Initial Values`

— Initial values of structure parameters

0 | parameter values

Specify initial values for the estimation and whether these values are to be fixed
or estimated. The values to specify depend on the model structure and your
specifications for **Delay** and **Zero**. Below
**Specify model structure**, the task displays the equation that
represents the specified system. This equation contains all of the parameters that can
be estimated, and that you can initialize or fix. The possible parameters are:

*Kp*— Static gain*T*— Time constant for first real pole_{p1}*T*— Time constant for second real pole_{p2}*Tω*— Time constant for complex poles, equal to the inverse of the natural frequency*ζ*— Damping coefficient for complex poles*T*— Transport delay_{d}*T*— Time constant for the process zero_{z}

All time-based parameters are in the time units you select for
**Sample Time**.

**Specify Optional Parameters**

`Fit Focus`

— Minimize prediction error or simulation error

`Prediction`

(default) | `Simulation`

Fit focus specifies what error to minimize in the loss function during estimation.

`Prediction`

— Minimize the one-step-ahead prediction error between measured and predicted outputs. This estimation approach focuses on producing a good predictor model for the estimation inputs and outputs. Prediction focus generally produces the best estimation results because it uses both input and output measurements, thus accounting for disturbances.`Simulation`

— Minimize the error between measured and simulation outputs. This estimation approach focuses on producing a simulated model response that has a good fit with the estimation inputs and outputs. Simulation focus is generally best for validation, especially with data sets not used for the original estimation.

`Initial Conditions`

— Handling of initial conditions

`Auto`

(default) | `Zero`

| `Estimate`

| `Backcast`

Set this option when you want to choose a specific method for initializing the
model. With the default setting of `Auto`

, the software chooses
the method based on the estimation data. Choices are:

`Zero`

— The initial state is set to zero.`Estimate`

— The initial state is treated as an independent estimation parameter.`Backcast`

— The initial state is estimated using the best least squares fit.

`Input Intersampling`

— Intersampling behavior for input signal

`Zero-order hold`

(default) | `Triangle approximation`

| `Band-limited`

Input intersampling is a property of the input data. The task uses this property
when estimating process models. Specify **Input Intersampling** when
your data type is `Time`

or
`Frequency`

. If you are using an `iddata`

object, the object already contains the intersampling information. Choices for this
property are:

`Zero-order hold`

— Piecewise-constant input signal between samples`Triangle approximation`

— Piecewise-linear input signal between samples, also known as first-order hold`Band-limited`

— Input signal has zero power above the Nyquist frequency

`Search Method`

— Numerical search mode for iterative parameter estimation

`Auto`

(default) | `Gauss-Newton`

| `Adaptive Gauss-Newton`

| `Levenberg-Marquardt`

| `Gradient Search`

`Auto`

— For each iteration, the software cycles through the methods until it finds the first direction descent that leads to a reduction in estimation cost.`Gauss-Newton`

— Subspace Gauss-Newton least-squares search.`Levenberg-Marquardt`

— Levenberg-Marquardt least-squares search.`Adaptive Gauss-Newton`

—Adaptive subspace Gauss-Newton search.`Gradient Search`

— Steepest descent least-squares search.

`Max. Iterations`

— Maximum number of iterations during error minimization

20 (default) | positive integer

Set the maximum number of iterations during error minimization. The iterations stop
when **Max. Iterations** is reached or another stopping criterion is
satisfied, such as **Tolerance**.

`Tolerance`

— Minimum percentage of expected improvement in error

0.01 (default) | positive integer

When the percentage of expected improvement is less than
**Tolerance**, the iterations stop.

`Weighting Prefilter`

— Weighting prefilter for loss function

`No filter`

(default) | `Passband(s)`

| `LTI Filter`

| `Frequency weights vector`

Set this option when you want to apply a weighting prefilter to the loss function that the task minimizes when you estimate the model. When you select an option, you must also select the associated variable in your workspace that contains the filter information. The available options depend on the domain of the data.

Weighting Prefilter | Data Domain | Filter Information |
---|---|---|

`No Filter` | Time and frequency | |

`Passbands` | Time and frequency | Passband ranges, specified as a 1-by-2 row vector or an
n-by-2 matrix, where n is the number
of passbands |

`LTI Filter` | Time and frequency | SISO LTI model |

`Frequency Weights Vector` | Frequency | Frequency weights, specified as a column vector with the same length as the frequency vector |

For instance, suppose that you are performing estimation with SISO
frequency-domain data and that in your MATLAB workspace, you have a column vector `W`

that contains
frequency weights for the prefilter. In the task, select ** Weighting
prefilter > Frequency weights vector** and the variable
`W`

.

**Visualize Results**

`Output Plot`

— Plot comparison of model and measured outputs

on (default) | off

Plot a comparison of the model output and the original measured data, along with the fit percentage. If you have separate validation data, a second plot compares the model response to the validation input data with the measured output from the validation data set.

## Version History

**Introduced in R2019b**

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