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.
Identifying linear black-box models from single-input/single-output (SISO) data using the System Identification app.
Identifying linear models from multiple-input/single-output (MISO) data using System Identification Toolbox commands.
Identifying continuous-time transfer functions from single-input/single-output (SISO) data using the System Identification app.
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.
Identifying nonlinear black-box models from single-input/single-output (SISO) data using the System Identification app.
System identification is a methodology for building mathematical models of dynamic systems using measurements of the system’s input and output signals.
Summary of typical tasks in the system identification workflow.
System Identification Toolbox software supports estimation of linear models from both time- and frequency-domain data.
Types of continuous-time and discrete-time models you can estimate from time- and frequency-domain data.
Overview of frequency-domain identification in the toolbox.
When to use the app versus the System Identification Toolbox commands.
Working with System Identification App.
Summary of commands for constructing models.
Estimate states and parameters of a system in real-time.