System Identification Toolbox™ 7.2

Product Description

• Introduction and Key Features
• Working with System Identification Toolbox
• Working with Measured Data
• Estimating Parametric Models
• Validating Results

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.

Data analysis and preprocessing. You can analyze time- and frequency-domain data and perform preprocessing tasks, such as filtering and detrending. Click on image to see enlarged view.