|System Identification||Identify models of dynamic systems from measured data|
|Estimate impulse response using prewhitened-based correlation analysis|
|Nonparametric impulse response estimation|
|Estimate state-space model from impulse response data using Eigensystem Realization Algorithm (ERA)|
|Obtain model parameters and associated uncertainty data|
|Modify values of model parameters|
|Options set for |
Examples and How To
- 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
impulseestcommand 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.