Learn more about System Identification Toolbox

Overview of Model Validation and Plots

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When to Validate Models Ways to Validate Models Data for Validating Models Supported Model Plots Plotting Models in the GUI Getting Advice About Models

When to Validate Models

After estimating each model, you can validate whether the model reproduces system behavior within acceptable bounds. You iterate between estimation and validation until you find the simplest model that best captures the system dynamics.

For ideas on how to adjust your modeling strategy based on validation results, see Troubleshooting Models.

Tip   If you have installed the Control System Toolbox product, you can also view models using the LTI Viewer. For more information, see Viewing Model Response Using the LTI Viewer.

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Ways to Validate Models

You can use the following approaches to validate models:

• Comparing simulated or predicted model output to measured output.

  See Simulating and Predicting Model Output.

  To simulate identified models in the Simulink environment, see Simulating Model Output.

• Analyzing autocorrelation and cross-correlation of the residuals with input.

  See Residual Analysis.

• Analyzing model response. For more information, see the following:

  • Impulse and Step Response Plots

  • Frequency Response Plots

  For information about the response of the noise model, see Noise Spectrum Plots.

• Plotting the poles and zeros of the linear parametric model.

  For more information, see Pole and Zero Plots.

• Comparing the response of nonparametric models, such as impulse-, step-, and frequency-response models, to parametric models, such as linear polynomial models, state-space model, and nonlinear parametric models.

  Note   Do not use this comparison when feedback is present in the system because feedback makes nonparametric models unreliable. To test if feedback is present in the system, use the advice command on the data.

• Compare models using Akaike Information Criterion or Akaike Final Prediction Error.

  For more information, see the aic and fpe reference page.

• Plotting linear and nonlinear blocks of Hammerstein-Wiener and nonlinear ARX models.

  For more information, see Hammerstein-Wiener Model Plots and Nonlinear ARX Model Plots.

Displaying confidence intervals on supported plots helps you assess the uncertainty of model parameters. For more information, see Computing Model Uncertainty.

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Data for Validating Models

For plots that compare model response to measured response, such as model output and residual analysis plots, you designate two types of data sets: one for estimating the models (estimation data), and the other for validating the models (validation data). Although you can designate the same data set to be used for estimating and validating the model, you risk overfitting your data. When you validate a model using an independent data set, this process is called cross-validation.

Note   Validation data should be the same in frequency content as the estimation data. If you detrended the estimation data, you must remove the same trend from the validation data. For more information about detrending, see Handling Offsets and Trends in Data.

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Supported Model Plots

The following table summarizes the types of supported model plots.

Plot Type	Supported Models	Learn More
Model Output	All linear and nonlinear models	Simulating and Predicting Model Output
Residual Analysis	All linear and nonlinear models	Residual Analysis
Transient Response	All linear parametric models Correlation analysis (nonparametric) models For nonlinear models, only step response.	Impulse and Step Response Plots
Frequency Response	All linear parametric models Spectral analysis (nonparametric) models	Frequency Response Plots
Noise Spectrum	All linear parametric models Spectral analysis (nonparametric) models	Noise Spectrum Plots
Poles and Zeros	All linear parametric models	Pole and Zero Plots
Nonlinear ARX	Nonlinear ARX models only	Nonlinear ARX Model Plots
Hammerstein-Wiener	Hammerstein-Wiener models only	Hammerstein-Wiener Model Plots

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Plotting Models in the GUI

To create one or more plots of your models, select the corresponding check box in the Model Views area of the System Identification Tool GUI. An active model icon has a thick line in the icon, while an inactive model has a thin line. Only active models appear on the selected plots.

To include or exclude a model on a plot, click the corresponding icon in the System Identification Tool GUI. Clicking the model icon updates any plots that are currently open.

For example, in the following figure, Model output is selected. In this case, the models n4s4 is not included on the plot because only arx441 is active.

Plots Include Only Active Models

To close a plot, clear the corresponding check box in the System Identification Tool GUI.

Tip   To get information about a specific plot, select a help topic from the Help menu in the plot window.

For general information about working with plots in the System Identification Toolbox product , see Working with Plots in the System Identification Tool GUI.

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Getting Advice About Models

Use the advice command on an estimated model to answer the following questions about the model:

• Should I increase or decrease the model order?

• Should I estimate a noise model?

• Is feedback present?

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Model Analysis

Simulating and Predicting Model Output

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