Correlation Models

Impulse-response models obtained using correlation analysis

Applications

Identify models of dynamic systems from measured data

`cra`	Estimate impulse response using input/output data prewhitening before correlation analysis
`impulseest`	Nonparametric impulse response estimation
`era`	Estimate state-space model from impulse response data using Eigensystem Realization Algorithm (ERA) (depuis R2022b)

`getpvec`	Obtain model parameters and associated uncertainty data
`setpvec`	Modify values of model parameters

impulseestOptions Options set for impulseest

Estimate Impulse-Response Models Using System Identification App
Estimate in the app using time-domain correlation analysis.
Estimate Impulse-Response Models at the Command Line
Use impulseest command to estimate using correlation analysis.
Compute Response Values
Obtain numerical impulse- and step-response vectors as a function of time.
Identify Delay Using Transient-Response Plots
You can use transient-response plots to estimate the input delay, or dead time, of linear systems.

What Is Time-Domain Correlation Analysis?
Time-domain correlation analysis refers to non-parametric estimation of the impulse response of dynamic systems as a finite impulse response (FIR) model from the data.
Data Supported by Correlation Analysis
Characteristics of data supported for estimation of impulse-response models.
Correlation Analysis Algorithm
Correlation analysis refers to methods that estimate the impulse response of a linear model, without specific assumptions about model orders.