System Identification Toolbox

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.