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Simulation and Prediction

Simulate or predict response of identified models; import identified models in Simulink® using model simulation blocks

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 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


Simulation and Prediction

Simulating and Predicting Model Output

Understanding the difference between simulated and predicted output.

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.

Simulating Identified Model Output in Simulink

Blocks for importing and simulating 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 the Output of a Dynamic System

Workflow for forecasting time series data and input-output data using linear and nonlinear models.

Perform Multivariate Time Series Forecasting

This example shows how to perform multivariate time series forecasting of data measured from predator and prey populations in a prey crowding scenario.

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