Model Type and Other Transformations
|Frequency response data or model|
|Polynomial model with identifiable parameters|
|Transfer function model with identifiable parameters|
|State-space model with identifiable parameters|
|Canonical state-space realization|
|Model order reduction|
|Transform identified linear model with noise channels to model with measured channels only|
|Translate parameter covariance across model transformation operations|
|Merge estimated models|
|Group models by appending their inputs and outputs|
|Noise component of model|
|Replace time delays by poles at z = 0 or phase shift|
|Change time units of dynamic system|
|Change frequency units of frequency-response data model|
|Delete specified data from frequency response data (FRD) models|
|Build model array by stacking models or model arrays along array dimensions|
|State coordinate transformation for state-space model|
Examples and How To
- Transforming Between Linear Model Representations
Converting between state-space, polynomial, and frequency-response representations.
- Reducing Model Order Using Pole-Zero Plots
You can use pole-zero plots of linear identified models to evaluate whether it might be useful to reduce model order.
- Create and Plot Identified Models Using Control System Toolbox Software
Identify models and use the Linear System Analyzer to plot the models.
- Using Identified Models for Control Design Applications
Using System Identification Toolbox™ models with Control System Toolbox™ software.
- Subreferencing Models
Creating models with subsets of inputs and outputs from multivariable models at the command line.
- Canonical State-Space Realizations
Modal, companion, observable and controllable canonical state-space models.
- Concatenating Models
Horizontal and vertical concatenation of model objects at the command line.
- Merging Models
How to merge models to obtain a single model with parameters that are statistically weighed means of the parameters of the individual models.
- Treating Noise Channels as Measured Inputs
Convert noise channels to measured channels and include the variance of the innovations.