| System Identification Toolbox | |
To provide information about the results of the estimation process
Syntax
Description
Any estimated model has the property EstimationInfo, which is a structure whose fields give information about the results of the estimation. The model structure will contain the properties ParameterVector, CovarianceMatrix, and NoiseVariance, which are all calculated in the estimation process (see the reference page for idmodel). In addition, EstimationInfo contains the following fields:
Status: Information whether the model has been estimated, or modified after being estimated.
Method: The name of the estimation command that produced the model.
LossFcn: The value of the identification criterion at the estimate. Normally equal to the determinant of the covariance matrix of the prediction errors, i.e., the determinant of NoiseVariance. Note that the loss function for the minimization might be different due to LimitError. In LossFcn, always the value of the non-robustified loss function is stored.
FPE: Akaikes Final Prediction Error, defined as LossFcn *(1+d/N}/(1-d/N) where d is the number of estimated parameters and N is the length of the data record.
DataName: Name of the data set from which the model was estimated. This is equal to the property name of the iddata object. If this was not defined, the name of the MATLAB iddata variable is used.
DataLength: The length of the data record
DataTs: The sampling interval of the data
DataInterSample: The intersample behavior of the data from which the model was estimated. This equals the property InterSample of the iddata object. (See iddata.)
WhyStop: For models that have been estimated by iterative search. The stopping rule that caused the iterations to terminate. Assumes values like 'MaxIter reached','No improvement possible along the search vector' or 'Near (local) minimum'. The latter means that the expected improvement is less than Tolerance (see Algorithm Properties).
UpdateNorm: The norm of the Gauss-Newton vector at the last iteration
LastImprovement: The relative improvement of the criterion value at the last iteration.
Iterations: The number of iterations used in the search.
InitialState: The actually used option when Model.InitialState = 'auto'.
N4Weight: For n4sid estimates, or estimates that have been initialized by n4sid: the actual value of N4Weight used.
N4Horizon: For n4sid estimates, or estimates that have been initialized by n4sid: the actual value of N4Horizon used. See n4sid and Algorithm Properties.
See Also
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