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 theSystem Identificationapp and usingsim. You can predict the model response a certain time horizon into the future using past measurements of inputs and outputs. Usepredictto predict model response over the time span of the measured data, and useforecastto 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.

Functions

`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 to support system identification

Blocks

Iddata Source	Import time-domain data stored in`iddata`object inMATLABworkspace
Iddata Sink	Export simulation data as`iddata`object toMATLABworkspace
Idmodel	Simulate identified linear model in万博1manbetx软件
Nonlinear ARX Model	Simulate nonlinear ARX model in万博1manbetx软件
Hammerstein-Wiener模型	Simulate Hammerstein-Wiener model in万博1manbetx软件
Nonlinear Grey-Box Model	Simulate nonlinear grey-box model in万博1manbetx软件

Topics

Simulation and Prediction

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.

Forecasting

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.