Nonlinear fit with constraints in R2012b
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Hi,
Is it possible to get parameter estimation variances after constrained optimization?
I am now using lsqnonlin function to optimize 7 parameters with 14 target values, using 'trust-region-reflective' algorithm.
I referred to the following threads, but they seems like mentioning on unconstrained optimizations. http://www.mathworks.com/matlabcentral/answers/51136 http://www.mathworks.com/matlabcentral/answers/45232 http://www.mathworks.com/matlabcentral/newsreader/view_thread/314454
Thanks in advance
2 Comments
Accepted Answer
Matt J
on 12 Jan 2013
If none of the constraints are active at your solution, you can estimate the variances in the unconstrained way.
Otherwise, since it's a pretty small problem, why not just estimate the parameter variance by running Monte Carlo simulations?
6 Comments
Matt J
on 13 Jan 2013
Edited: Matt J
on 13 Jan 2013
Anyway, what I was picturing for the simulations was that you assume the parameter estimates given by lsqnonlin are the true values. Then simulate the system measurements with as realistic measurement noise as you can. Then rerun lsqnonlin on these measurements. Do that repeatedly until you have enough data to approximate the variance well.
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