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Uncertainty Analysis

Compute parameter variability, plot confidence bounds

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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.

Functions

present Display model information, including estimated uncertainty
simsd Simulate linear models with uncertainty using Monte Carlo method
freqresp Frequency response over grid
rsample Random sampling of linear identified systems
showConfidence Display confidence regions on response plots for identified models
getcov Parameter covariance of linear identified parametric model
setcov Set parameter covariance data in identified model
translatecov Translate parameter covariance across model operations
step Step response plot of dynamic system
stepplot Plot step response and return plot handle
impulse Impulse response plot of dynamic system; impulse response data
bode Bode plot of frequency response, magnitude and phase of frequency response
bodemag Bode magnitude response of LTI models
nyquist Nyquist plot of frequency response
nyquistplot Nyquist plot with additional plot customization options
iopzmap Plot pole-zero map for I/O pairs of model
iopzplot Plot pole-zero map for I/O pairs and return plot handle
tfdata Access transfer function data
zpkdata Access zero-pole-gain data
simsdOptions Option set for simsd

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