System Identification Toolbox

Common Terms Used in System Identification

This section defines some of the terms that are frequently used in System Identification:

• Estimation Data is the data set that is used to fit a model to data. In the GUI this is the same as the Working Data.
• Validation Data is the data set that is used for model validation purposes. This includes simulating the model for these data and computing the residuals from the model when applied to these data.
• Model Views are various ways of inspecting the properties of a model. They include looking at zeros and poles, transient and frequency response, and similar things.
• Data Views are various ways of inspecting properties of data sets. A most common and useful thing is just to plot the data and scrutinize it.
  So-called outliers could be detected then. These are unreliable measurements, perhaps arising from failures in the measurement equipment. The frequency contents of the data signals, in terms of periodograms or spectral estimates, is also most revealing to study.
• Model Sets or Model Structures are families of models with adjustable parameters. Parameter Estimation amounts to finding the "best" values of these parameters. The System Identification problem amounts to finding both a good model structure and good numerical values of its parameters.
• Parametric Identification Methods are techniques to estimate parameters in given model structures. Basically it is a matter of finding (by numerical search) those numerical values of the parameters that give the best agreement between the model's (simulated or predicted) output and the measured one.
• Nonparametric Identification Methods are techniques to estimate model behavior without necessarily using a given parametrized model set.
  Typical nonparametric methods include Correlation analysis, which estimates a system's impulse response, and Spectral analysis, which estimates a system's frequency response.
• Model Validation is the process of gaining confidence in a model. Essentially this is achieved by "twisting and turning" the model to scrutinize all aspects of it. Of particular importance is the model's ability to reproduce the behavior of the Validation Data sets. Thus it is important to inspect the properties of the residuals from the model when applied to the Validation Data.

The System Identification Problem

Basic Information About Dynamic Models