Compare Output with Measured Data

Plot simulated or predicted output and measured data for comparison, compute best fit values

When you identify a model, you can simulate or predict the model response, and compare that response with measured input/output data. This comparison helps you choose among candidate models, and also aids you in validating the identified model you selected. You can plot model response alongside measured output data, while using initial conditions that can be estimated from the data. You can also compute a metric that quantifies how well your model response matches the measured output data.

Functions

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Compare Output and Measured Data

`compare`	Compare identified model output with measured output
`compareOptions`	Option set for `compare`
`goodnessOfFit`	Goodness of fit between test and reference data for analysis and validation of identified models
`predict`	Predict identified model K-step-ahead output
`predictOptions`	Option set for `predict`
`sim`	Simulate response of identified model
`simOptions`	Option set for `sim`

Estimate Initial Conditions

`initialCondition`	Initial condition representation for linear time-invariant systems (Since R2020b)
`findstates`	Estimate initial states of model
`findstatesOptions`	Option set for `findstates`
`data2state`	Map past data to states of state-space and nonlinear ARX models
`idpar`	Create parameter for initial states and input level estimation

Topics

Compare Model Output to Measured Data

Compare Simulated Output with Measured Validation Data
This example shows how to validate an estimated model by comparing the simulated model output with measured data that was not used for the original estimation.
Plot Models in the System Identification App
To create one or more plots of your models, select the corresponding check box in the Model Views area of the System Identification app.

Simulate or Predict Model Response

Simulate and Predict Identified Model Output

Understand the difference between simulated and predicted output and when to use each.

Set Initial Conditions

Estimate Initial Conditions for Simulating Identified Models
Estimate initial conditions for use in simulations executed from the command line, from Simulink^®, and from the System Identification app.
Apply Initial Conditions When Simulating Identified Linear Models
This example describes the workflow for obtaining estimated initial conditions (ICs) for a transfer function model and using the ICs when simulating the model. The example also shows how to use ICs when you convert a model from one model type to another.
Reproduce Command Line or System Identification App Simulation Results in Simulink
Resolve differences between simulation results when comparing command-line or System Identification model outputs with Simulink outputs.

Plot Data

Supported Model Plots

Examine available plot types and corresponding supported models.
- Impulse and Step Response Plots
- Frequency Response Plots for Model Validation
- Noise Spectrum Plots
- Pole and Zero Plots
Compute Model Uncertainty
Compute model parameter uncertainty of linear models.
Specify Toolbox Preferences for Linear Analysis Plots
Set linear analysis plot preferences that persist between MATLAB^® sessions.