||Simulate response of identified model|
||Option set for sim|
||Simulate linear models with uncertainty using Monte Carlo method|
||Option set for simsd|
||Predict K-step ahead model output|
||Option set for predict|
||Random sampling of linear identified systems|
||Forecast identified model output|
||Option set for forecast|
||Generate input signals|
|IDDATA Sink||Export iddata object to MATLAB workspace|
|IDDATA Source||Import iddata object from MATLAB workspace|
|IDMODEL Model||Simulate identified linear model in Simulink software|
|IDNLARX Model||Simulate nonlinear ARX model in Simulink software|
|IDNLGREY Model||Simulate nonlinear grey-box model in Simulink software|
|IDNLHW Model||Simulate Hammerstein-Wiener model in Simulink software|
To create a model output plot for parametric linear and nonlinear models in the System Identification app, select the Model output check box in the Model Views area.
If you estimated a linear model from detrended data and want
to simulate or predict the output at the original operation conditions,
retrend to add trend data
back into the simulated or predicted output.
This example shows how you can create input data and a model, and then use the data and the model to simulate output data.
This example shows how to simulate a continuous-time state-space model using a random binary input
u and a sample time of
Workflow for forecasting time series data and input-output data using linear and nonlinear models.
This example shows how to perform multivariate time series forecasting of data measured from predator and prey populations in a prey crowding scenario.
This example shows how to validate an estimated model by comparing the simulated model output with measured data.
Understanding the difference between simulated and predicted output.
Description of the System Identification Toolbox™ block library.
Blocks for importing and simulating models from the MATLAB® environment into a Simulink model.
Understand the concept of forecasting data using linear and nonlinear models.