[SciPy-user] Parameter estimation / Data fitting in scipy
My VDI Freemail
wagner.nils at vdi.de
Sat Aug 24 15:11:38 CDT 2002
Thank you for your prompt reply. Please can you send
me the missing module
Test: System Identification
Traceback (most recent call last):
File "hansen.py", line 96, in ?
from test.ode_system import TestOdeSystem
ImportError: No module named ode_system
> I've just done this recently. I't a classical "(dynamic) system
> identification problem" consiting
> of a nonlinear least-squares problem with an ode that needs to be
> integrated repeatedly in the iteration loop.
> Optimally, you would be able to put bounds on the parameters guiding the
> solution of the nonlinear
> optimization process. Unfortunately, scipy doesn't provide such a
> yet for vector systems
> (for the future a python interface to e.g. Omuses/HQP could provede a
> solution) so that for the moment
> we're stuck with unbounded optimization --- this may/will work with not
> too many parameters in vector
> p and a good conditioned system.
> An example is attached. Success.
> Henk Jansen
> Nils Wagner wrote:
> > Hi,
> > Suppose it is desired to fit a set of data y_i to a known model
> > [given in form of an IVP (initial value problem)]
> > y' = f(y,p,t) , y(0) = y_0(p), y' = dy/dt
> > where p is a vector of parameters for the model that need to be found.
> > y denotes the state-variables. The initial conditions y(0) may also
> > depend on
> > parameters.
> > How can I solve this problem using scipy's optimization and ode tools ?
> > A small example would be appreciated.
> > Thanks in advance
> > Nils
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