Parametric Model Estimation Methods (System Identification Toolkit)
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 LabVIEW System Identification Toolkit. 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 |