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Uncertainty Analysis

Compute parameter variability, plot confidence bounds

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.


presentDisplay model information, including estimated uncertainty
simsdSimulate linear models with uncertainty using Monte Carlo method
freqrespEvaluate system response over a grid of frequencies
rsampleRandom sampling of linear identified systems
showConfidenceDisplay confidence regions on response plots for identified models
getcovParameter covariance of identified model
setcovSet parameter covariance data in identified model
translatecovTranslate parameter covariance across model transformation operations
stepStep response of dynamic system
stepplotPlot step response with additional plot customization options
impulseImpulse response plot of dynamic system; impulse response data
bodeBode plot of frequency response, or magnitude and phase data
bodemag Magnitude-only Bode plot of frequency response
nyquistNyquist plot of frequency response
nyquistplotNyquist plot with additional plot customization options
iopzmapPlot pole-zero map for I/O pairs of model
iopzplotPlot pole-zero map for I/O pairs with additional plot customization options
tfdataAccess transfer function data
zpkdataAccess zero-pole-gain data
simsdOptionsOption set for simsd

Examples and How To