System Identification Toolbox

Overview

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.

The principal architect of the toolbox is Professor Lennart Ljung, a recognized leader in the field of system identification.

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

Using System Identification Toolbox (top) to import, analyze, and preprocess data (left), estimate linear and nonlinear models (bottom), and validate estimated models (right).