Regularization in parameter estimation
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I'm estimating a nonlinear grey-box model with pem (estimator 'lsqnonlin'). I would like to add a regularization factor to the optimization, to penalize high-variance estimates.
I tried, following http://www.mathworks.it/it/help/ident/ref/idnlgrey.html , to set up the L2 regularization in this way, being nlgr my idnlgrey object:
nlgr.Algorithm.Regularization.Lambda = 10;
I didn't explicitly set the weighting matrix for Lambda, as from the documentation it should transparently defaults as mat(1) (which is what i want). I also tried other (higher) values for Lambda, but apparently the results are identical with or without this setting, and I obtain very high varianced estimates.
Am I missing something?
Note: I am using Matlab v. 2013a
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