[scilab-Users] fitting ODE solution to experimental data

[Adrien Vogt-Schilb]

Hi

The objective function should be a real number, I believe.
If this si true, it should not have the same size than the data vector 
(it should be 1x1 real number)

May be you could use a norm of the cost function you are currently 
using. Do that using scilab function norm.

Hope it helps

On 17/08/2011 17:09, Peter Hinow wrote:
> Dear fellows,
>
> is there a "canonical" way to fit the solution of a parameter-dependent ordinary differential equation y' = f(y;p) to some experimental data?
>
> I have tried leastsq, but the optimal solution is always the initial guess, regardless of the initial guess. Specifically, I'm not able to get any information why leastsq terminates, even when I add the option "imp=2" (the "optimal" gradient has an entry of order 10^21). When I try datafit instead, I get an error like
>
> Submatrix incorrectly defined.
> at line      26 of function costf called by :
> at line     174 of function datafit called by :
>
> even though the output of the objective function has the same size as the data vector.
>
> Thank you very much for your help,
>       Peter
>


-- 

*Adrien Vogt-Schilb*
Research Fellow
vogt at centre-cired.fr
Tel: (+33) 1 43 94 73 96
Fax: (+33) 1 43 94 73 70

CIRED

	

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