Hi there shinkoola,
You really haven't given us much context around the problem or the code that resulted in your maple output.
I fear without this your question will go unanswered.
I am studying a paper about metal forming and I have a problem about data fitting. this problem has 18 pair of inputs (x,y) that are obtained from experiment and has 9 constants. I want to obtain this constants. I attached file.I am trying to solve that by using of Maple v.13. In fact at the mentioned paper , authors have obtained these constants and I don't understand how. I'm waiting your help.
Thank u your response. I attached Maple file and its pdf format. Please see that and tell me about my mistakes.
thank u vary much
I have uploaded those and these are their links :
Hadi Mahdipour.mw - 4shared.com - online file sharing and storage - download
Hadi Mahdipour.pdf - 4shared.com - document sharing - download
We want to fit our function y(x) with our data. and that function has some constants and if we fit then we can obtain values of our constants (a,b,A,B,m,n,p,q). But in this problem, Maple can not fit and I don't understand its cause. maybe this fitting operation is not true.!!!! Even I use Datafit software and it is not able to solve this problem.
Are there any constraints on the constants? If so, please list them out in full.
Have you tried the Excel Solver routine? That's quite a powerful nonlinear optimizer, and can work in quite a few situations. It can handle inequalities.
Another question: are you doing least squares fit here? That will make a difference as to how you set up your spreadsheet.
we have a constrain and that is: k<1/3. So about least square, I thought that that method is for linear problems and because of our problem is nonlinear , I am not sure that we can use that. and about your another questions I haven't tried Excel in the field of regression.But I have used Datafit.v.9 and that is pretty useful software to fit data. But this softwae couldn't solve this problem.
from which we see that there are in fact only four independent constants in your problem, so you are unlikely to be able to solve for all nine constants in your original form.