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.
Learn the basics of System Identification Toolbox
Plot, analyze, detrend, and filter time- and frequency-domain data, generate and import data
Identify impulse-response, frequency-response and parametric models, such as state-space and transfer function models
Identify nonlinear ARX, Hammerstein-Wiener, and grey-box models
Estimate coefficients of linear and nonlinear differential, difference and state-space equations
Compare model to measured output, residual analysis, response plots with confidence bounds
Discretize models, convert models to other types, linearize nonlinear models, simulate and predict output
Analyze time series data by identifying linear and nonlinear models, including AR, ARMA, and state-space models; forecast values
Estimate model parameters and states during system operation, generate code and deploy to embedded targets