You can estimate linear, black-box polynomial models from data with the following characteristics:
Time- or frequency-domain data (iddata or idfrd data objects).
Note
For frequency-domain data, you can only estimate ARX and OE models.
To estimate polynomial models for time-series data, see Time Series Analysis.
Real data or complex data in any domain.
Single-output and multiple-output.
You must import your data into the MATLAB® workspace, as described in Data Preparation.
To get a linear, continuous-time model of arbitrary structure for time-domain data, you
can estimate a discrete-time model, and then use d2c to transform it to a continuous-time model.
For continuous-time frequency-domain data, you can estimate directly only Output-Error (OE) continuous-time models. Other structures include noise models, which is not supported for frequency-domain data.
Tip
To denote continuous-time frequency-domain data, set the data sample time to 0. You
can set the sample time when you import data into the app or set the Ts
property of the data object at the command line.
You can estimate arbitrary-order, linear state-space models for both time- or frequency-domain data.
Set the data property Ts to:
0, for frequency response data that is measured directly from an
experiment.
Equal to the Ts of the original data, for frequency response data
obtained by transforming time-domain iddata (using
spa and etfe).
Tip
You can set the sample time when you import data into the app or set the
Ts property of the data object at the command line.