仿真与预测

仿真或预测辨识模型的响应；使用模型仿真模块在 Simulink^® 中导入辨识模型

您可以在系统辨识中使用 sim 仿真辨识模型对给定输入的响应。您可以使用过去的输入和输出测量值来预测模型在未来某个时间范围内的响应。使用 predict 可预测模型在测量数据时间范围内的响应，使用 forecast 可在没有测量数据的情况下预测模型在未来时间范围内的响应。您还可以将辨识模型导入 Simulink，并使用模型仿真模块对模型响应进行仿真。

函数

`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`	预测辨识模型的 K 步输出
`predictOptions`	Option set for `predict`
`forecast`	Forecast time-series values into future
`forecastOptions`	Option set for `forecast`
`idinput`	Generate input signals to support system identification

模块

Iddata Source	Import time-domain data stored in `iddata` object in MATLAB workspace
Iddata Sink	将仿真数据作为 `iddata` 对象导出到 MATLAB 工作区
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

主题

仿真与预测

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.

预测

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.