Get Started with System Identification Toolbox
System Identification Toolbox™ provides MATLAB® functions, Simulink® blocks, and an app for constructing mathematical models of dynamic systems from measured input-output data. It lets you create and use models of dynamic systems not easily modeled from first principles or specifications. You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process models, and state-space models. The toolbox also provides algorithms for embedded online parameter estimation.
The toolbox provides identification techniques such as maximum likelihood, prediction-error minimization (PEM), and subspace system identification. To represent nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. The toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting.
- Identify Linear Models Using System Identification App
Identify linear black-box models from single-input/single-output (SISO) data using the System Identification app.
- Identify Linear Models Using the Command Line
Identify linear models from multiple-input/single-output (MISO) data using System Identification Toolbox commands.
- Identify Low-Order Transfer Functions (Process Models) Using System Identification App
Identify continuous-time transfer functions from single-input/single-output (SISO) data using the app.
- Estimate Continuous-Time Grey-Box Model for Heat Diffusion
This example shows how to estimate the heat conductivity and the heat-transfer coefficient of a continuous-time grey-box model for a heated-rod system.
- Identify Nonlinear Black-Box Models Using System Identification App
Identify nonlinear black-box models from single-input/single-output (SISO) data using the System Identification app.
About System Identification
- System Identification Overview
System identification is a methodology for building mathematical models of dynamic systems using measurements of the system’s input and output signals.
- System Identification Workflow
Summary of typical tasks in the system identification workflow.
- Supported Data
System Identification Toolbox software supports estimation of linear models from both time- and frequency-domain data.
- Supported Continuous- and Discrete-Time Models
Types of continuous-time and discrete-time models you can estimate from time- and frequency-domain data.
- Estimating Models Using Frequency-Domain Data
Overview of frequency-domain identification in the toolbox.
- When to Use the App vs. the Command Line
When to use the app versus the System Identification Toolbox commands.
- Working with System Identification App
Working with System Identification App.
- Commands for Model Estimation
Summary of commands for constructing models.
- What Is Online Estimation?
Estimate states and parameters of a system in real-time.