When you estimate the model parameters from data, you obtain their nominal values that are accurate within a confidence region. The size of this region is determined by the values of the parameter uncertainties computed during estimation. The magnitude of the uncertainties provide a measure of the reliability of the model. You can compute and visualize the effect of parameter uncertainties on the model response in time and frequency domains.
|Display model information, including estimated uncertainty|
|Simulate linear models with uncertainty using Monte Carlo method|
|Frequency response over grid|
|Random sampling of linear identified systems|
|Display confidence regions on response plots for identified models|
|Parameter covariance of identified model|
|Set parameter covariance data in identified model|
|Translate parameter covariance across model transformation operations|
|Step response plot of dynamic system; step response data|
|Plot step response and return plot handle|
|Impulse response plot of dynamic system; impulse response data|
|Bode plot of frequency response, or magnitude and phase data|
|Bode magnitude response of LTI models|
|Nyquist plot of frequency response|
|Nyquist plot with additional plot customization options|
|Plot pole-zero map for I/O pairs of model|
|Plot pole-zero map for I/O pairs and return plot handle|
|Option set for simsd|
To create a transient analysis plot in the System Identification app, select the Transient resp check box in the Model Views area.
To create a frequency-response plot for linear models in the System Identification app, select the Frequency resp check box in the Model Views area.
To create a noise spectrum plot for parametric linear models in the app, select the Noise spectrum check box in the Model Views area.
To plot the disturbance spectrum of an input-output model or
the output spectrum of a time series model, use
To create a pole-zero plot for parametric linear models in the System Identification app, select the Zeros and poles check box in the Model Views area.
Computing model parameter uncertainty of linear models.