Documentation

This is machine translation

Translated by Microsoft
Mouse over text to see original. Click the button below to return to the English verison of the page.

Simulation and Prediction

Simulate or predict model output using different inputs and in Simulink®

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
rsample Random sampling of linear identified systems
forecast Forecast identified model output
forecastOptions Option set for forecast
idinput Generate input signals

Blocks

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

Examples and How To

Simulation and Prediction in the App

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.

Simulation and Prediction at the Command Line

If you estimated a linear model from detrended data and want to simulate or predict the output at the original operation conditions, use retrend to add trend data back into the simulated or predicted output.

Simulate Model Output with Noise

This example shows how you can create input data and a model, and then use the data and the model to simulate output data.

Simulate a Continuous-Time State-Space Model

This example shows how to simulate a continuous-time state-space model using a random binary input u and a sample time of 0.1 s.

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.

Compare Simulated Output with Measured Data

This example shows how to validate an estimated model by comparing the simulated model output with measured data.

Concepts

Simulating and Predicting Model Output

Understanding the difference between simulated and predicted output.

Using System Identification Toolbox Blocks in Simulink Models

Description of the System Identification Toolbox™ block library.

Simulating Identified Model Output in Simulink

Blocks for importing and simulating models from the MATLAB® environment into a Simulink model.

Introduction to Forecasting of Dynamic System Response

Understand the concept of forecasting data using linear and nonlinear models.

Was this topic helpful?