System Identification Toolbox

Working with Measured Data

When preparing data for identification of models, you need to specify information such as input-output channel names, sampling interval, and intersample behavior. The toolbox lets you attach this information to the data using data objects. The data objects facilitate easy visualization of data, domain conversion, and various preprocessing tasks.

Measured data often has offsets, slow drifts, outliers, missing values, and other anomalies. The toolbox removes such anomalies by performing operations such as detrending, filtering, resampling, and reconstruction of missing data. The toolbox can analyze the suitability of data for identification and provide diagnostics regarding persistence of excitation, existence of feedback loops, intersample behavior, and presence of nonlinearities.

The toolbox produces estimates of step and frequency responses of the system directly from measured data. Using these responses, you can analyze system characteristics, such as time constants, input delays, and resonant frequencies. You can use this information to configure the parametric models during estimation.

Importing and Manipulating Data Sets 0:50
Import test data for estimating the model and validating results.

Preprocessing Data 1:15
View test data and remove disturbances and offsets.