Use linear model identification when a linear model is sufficient to completely capture your system dynamics. You can identify linear models in the System Identification app or at the command line. System Identification Toolbox™ enables you to create and estimate four general types of linear model.
Parametric models — Estimate parameters in structures such as transfer function models, state-space models, polynomial models, and process models.
Frequency-response models — Estimate spectral models using spectral analysis.
Correlation models — Perform nonparametric estimation of impulse-response models using correlation analysis.
Linear grey-box models — Estimate the coefficients of arbitrary ordinary differential or difference equations, incorporating system information that you obtain from prior knowledge or that you can deduce from physical principles.
Linear model identification requires frequency-domain or uniformly sampled time-domain data. Your data can have one or more input and output channels. For more information, see About Identified Linear Models. You can also model time series data, which contains a single output channel and no input channel, using parametric model structures such as AR and ARMA.
You can use the identified models to simulate and predict model output at the command line, in the app, or in Simulink®.