仿真与预测
R2025b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
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 | 生成输入信号以支持系统辨识 |
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 | 在 Simulink 软件中仿真非线性 ARX 模型 |
 | Hammerstein-Wiener Model | Simulate Hammerstein-Wiener model in Simulink software |
 | Nonlinear Grey-Box Model | 在 Simulink 软件中仿真非线性灰箱模型 |
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.
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.
