Time and again I've seen people claim they have local minima when they really have poorly converged optimizations.
I don't know anything about your case, but it is something to be looked out for.
On Dec 9, 2011, at 12/9/11 | 7:52 PM , Jason Moore wrote:
I was wondering any of you are system identification experts? I'm working with a linear grey box model of a closed loop bicycle and human controller with a large set of experimental data. We excited the the system with lateral perturbations and I now have pretty good results in fitting the model to our data. But there are at least a couple things I haven't quite figured out:
1. The grey box model optimization formulation with our 6 free parameters seems to have a lot of local minima. I've basically been giving a sweep of guesses based on manually fitting that work about half time. I'm looking for techniques in dealing with the local minima.
2. I'm interested in characterizing the process noise, i.e. human remnant, with a Kalman filter design. I have data for trials when the input is zero and feel like I can utilize it to characterize the noise, but my understanding of the subject is weak and haven't been able to put two and two together and make it work.
I'd love to chat about the specifics of my problem with anyone that may understand this really well.