Ways to Prepare Data for System Identification
Before you can perform any task in this toolbox, your data must be in the MATLAB® workspace. You can import the data from external data files or manually create data arrays at the command line. For more information about importing data, see Representing Data in MATLAB Workspace.
The following tasks help to prepare your data for identifying models from data:
Represent data for system identification
You can represent data in the format of this toolbox by doing one of the following:
For working in the app, import data into the System Identification app.
See Represent Data.
For working at the command line, create an
For time-domain or frequency-domain data, see Representing Time- and Frequency-Domain Data Using iddata Objects.
For frequency-response data, see Representing Frequency-Response Data Using idfrd Objects.
To simulate data with and without noise, see Generate Data Using Simulation.
You can analyze your data by doing either of the following:
Plotting data to examine both time- and frequency-domain behavior.
See How to Plot Data in the App and How to Plot Data at the Command Line.
advicecommand to analyze the data for the presence of constant offsets and trends, delay, possible feedback, and signal excitation levels.
Review the data characteristics for any of the following features to determine if there is a need for preprocessing:
Missing or faulty values (also known as outliers). For example, you might see gaps that indicate missing data, values that do not fit with the rest of the data, or noninformative values.
Offsets and drifts in signal levels (low-frequency disturbances).
See Handling Offsets and Trends in Data for information about subtracting means and linear trends, and Filtering Data for information about filtering.
High-frequency disturbances above the frequency interval of interest for the system dynamics.
See Resampling Data for information about decimating and interpolating values, and Filtering Data for information about filtering.
You can use data selection as a way to clean the data and exclude parts with noisy or missing information. You can also use data selection to create independent data sets for estimation and validation.
To learn more about selecting data, see Select Subsets of Data.
Combine data from multiple experiments
You can combine data from several experiments into a single data set. The model you estimate from a data set containing several experiments describes the average system that represents these experiments.
To learn more about creating multiple-experiment data sets, see Create Multiexperiment Data Sets in the App or Create Multiexperiment Data at the Command Line.
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