LabVIEW Control Design and Simulation Module

Parametric Model Estimation Methods (Advanced Signal Processing Toolkit or Control Design and Simulation Module)

  • Updated2023-03-14
  • 1 minute(s) read

Parametric models describe systems in terms of difference or differential equations, depending on whether a system is represented by a discrete or continuous model. Compared to nonparametric models, parametric models might provide a more accurate estimation if you have prior knowledge about the system dynamics to determine model orders, time delays, and so on.

The following table lists the representations of parametric models you can develop by using the System Identification VIs. Each representation supports one or more input-output configurations: single-input single-output (SISO), multiple-input single-output (MISO), and/or multiple-input multiple-output (MIMO).

SISO MISO MIMO
General-Linear X X
Autoregressive (AR) X
Autoregressive with exogenous terms (ARX) X X X
Autoregressive moving average with exogenous terms (ARMAX) X X
Box-Jenkins X X
Output-Error X X
Transfer Function X X
Zero-Pole-Gain X X
State-Space X X X

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