You can simulate the response of an identified model to given inputs in the System
Identification app and using sim. You can predict the model response a certain time horizon into the future
using past measurements of inputs and outputs. Use predict to predict model response over the time span of the measured data, and
use forecast to predict the response over a future time span when no measured data
is available. You can also import identified models to Simulink, and simulate model response using model simulation blocks.
sim | Simulate response of identified model |
simOptions | Option set for sim |
simsd | Simulate linear models with uncertainty using Monte Carlo method |
simsdOptions | Option set for simsd |
predict | Predict K-step-ahead model output |
predictOptions | Option set for predict |
forecast | Forecast identified model output |
forecastOptions | Option set for forecast |
idinput | Generate input signals |
| Iddata Source | Import time-domain data stored in iddata object in
MATLAB workspace |
| Iddata Sink | Export simulation data as iddata object to MATLAB workspace |
| Idmodel | Simulate identified linear model in Simulink software |
| Nonlinear ARX Model | Simulate nonlinear ARX model in Simulink software |
| Hammerstein-Wiener Model | Simulate Hammerstein-Wiener model in Simulink software |
| Nonlinear Grey-Box Model | Simulate nonlinear grey-box model in Simulink software |
Simulate and Predict Identified Model Output
Understand the difference between simulated and predicted output and when to use each.
Simulation and Prediction in the App
Perform simulation and prediction in the System Identification app, and interpret results.
Simulation and Prediction at the Command Line
Perform simulation, prediction, and forecasting at the command line, specify initial conditions.
Simulate Identified Model in Simulink
Use model blocks to import, initialize, and simulate models from the MATLAB® environment into a Simulink model.
Using System Identification Toolbox Blocks in Simulink Models
Description of the System Identification Toolbox™ block library.
Introduction to Forecasting of Dynamic System Response
Understand the concept of forecasting data using linear and nonlinear models.
Forecast Output of Dynamic System
Workflow for forecasting time series data and input-output data using linear and nonlinear models.
Forecast Multivariate Time Series
This example shows how to perform multivariate time series forecasting of data measured from predator and prey populations in a prey crowding scenario.