System Identification Toolbox0 pages

System Identification Toolbox
Create linear and nonlinear dynamic system models from measured input-output data
System Identification Toolbox™ constructs mathematical models of dynamic systems from measured
input-output data. It provides MATLAB® functions, Simulink® blocks, and an interactive tool for creating and
using 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 provides maximum likelihood, prediction-error minimization (PEM), subspace system identification,
and other identification techniques. For 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 prediction of system response and for simulation in Simulink. The toolbox also lets you
model time-series data and perform time-series forecasting.
Key Features
▪ Transfer function, process model, and state-space model identification using time-domain and
frequency-domain response data
▪ Autoregressive (ARX, ARMAX), Box-Jenkins, and Output-Error model estimation using maximum
likelihood, prediction-error minimization (PEM), and subspace system identification techniques
▪ Time-series modeling (AR, ARMA, ARIMA) and forecasting
▪ Identification of nonlinear ARX models and Hammerstein-Weiner models with input-output nonlinearities
such as saturation and dead zone
▪ Linear and nonlinear grey-box system identification for estimation of user-defined models
▪ Delay estimation, detrending, filtering, resampling, and reconstruction of missing data
▪ Blocks for using identified models in Simulink
The principal architect of the toolbox is Professor Lennart Ljung, a recognized leader in the field of system
identification.

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