|Compute and test residuals|
|Prediction error for identified model|
|Akaike’s Final Prediction Error for estimated model|
|Akaike’s Information Criterion for estimated model|
Create a residual analysis plot for linear and nonlinear models in the System Identification app.
Create a residual-analysis plot for linear and nonlinear models at the command line.
This example shows how you can use residual analysis to evaluate model quality.
Residuals are differences between the one-step-predicted output from the model and the measured output from the validation data set. Thus, residuals represent the portion of the validation data not explained by the model.