curve fitting a custom equation

11 次查看（过去 30 天）
Ayushi Sharma
Ayushi Sharma 2021-7-15
编辑： Alex Sha 2021-7-18
a*exp(-x/T) +c*(T*(exp(-x/T)-1)+x)
This is the equation i'm trying to fit using the custom fit tool. Since the variable x is both in exponential and linear form, the data isn't really fitting, it's just a straight line. Is there way to fit the data to this equation?
As a side note: how do I view the numerical values instead of scientific ones in curve fitting tool?
Thank you
  1 个评论
Matt J
Matt J 2021-7-16
编辑：Matt J 2021-7-16
@Ayushi Sharma
In the first line of your post, you show the model equation as,
a*exp(-x/T) +c*(T*(exp(-x/T)-1)+x)
However, in your screenshot of the cftool app, the model equation appears instead as,
a*exp(-x*T) +c*(T*(exp(-x/T)-1)+x)
You should edit your post so that we know which version of the equation is the intended one.

请先登录，再进行评论。

请先登录，再回答此问题。

采纳的回答

Alex Sha
Alex Sha 2021-7-17
编辑：Alex Sha 2021-7-18
Hi, all, as mentioned by Walter Roberson, the resule I provided above is obtained by using 1stOpt, the biggest advantage, for 1stOpt, is that the guessing of initial-start values are no longer needed for curve-fitting, equation-solving or any other optimization problem.
if the model function is: y=a*exp(-x*T) +c*(T*(exp(-x/T)-1)+x), the result is:
Root of Mean Square Error (RMSE): 15155741.0636478
Sum of Squared Residual: 4.59392974376683E15
Correlation Coef. (R): 0.996754359090101
R-Square: 0.993519252365118
Parameter Best Estimate
---------- -------------
a -33754000.9741343
t -1.00000000434611
c -33754006.8543357
Note the value of parameter t: t=-1.00000000434611, if taking t as -1.00, the chart will become:
The Sensitivity analysis of each parameter is as below:
while, if the model function is: y=a*exp(-x/T) +c*(T*(exp(-x/T)-1)+x), the result is:
Root of Mean Square Error (RMSE): 15137803.4043043
Sum of Squared Residual: 4.58306183814736E15
Correlation Coef. (R): 0.996383637725283
R-Square: 0.992780353526669
Parameter Best Estimate
---------- -------------
a -54592643.2783804
t -1.72718663083536
c -31608221.5261319
  2 个评论
Alex Sha
Alex Sha 2021-7-17
The result got by Matt J (as show below) is actually a local solution, not global one.
a =
-6.2820e+07
c =
-2.9980e+07
T =
1.0481e-14
Matt J
Matt J 2021-7-17
编辑：Matt J 2021-7-17
The solution I presented below constrained the search to T>=0, so it might be global subject to that constraint.
It's important to notice as well that whether T=-1 or T=0 is taken as the solution, it results in a cancelation of the exponential terms, leaving an essentially linear solution. It makes it seem like either the model was over-parametrized to begin with, or there isn't enough (x,y) data to accurately estimate the exponential terms.

请先登录，再进行评论。

更多回答（1 个）

Matt J
Matt J 2021-7-15
You don't need a custom type. Your model is just exp2 but with modified data (x,y-x)
  11 个评论
显示 9更早的评论
Walter Roberson
Walter Roberson 2021-7-16
Alex uses a commercial program named 1stOpt from 7d-soft. It costs roughly $2000 for a license. That is, of course, not exactly "cheap"; however, having seen the results Alex has posted on a number of different problems, I would say that if you do curve fitting, that it does a very nice job. I can sometimes improve slightly over the results it gets, but only after at least an hour of work.
Matt J
Matt J 2021-7-16
编辑：Matt J 2021-7-16
Thank you so much for your input. I have opted for this method.
@Ayushi Sharma You're welcome, but please Accept-click the answer if you consider the question addressed.
Alex uses a commercial program named 1stOpt from 7d-soft. ... I would say that if you do curve fitting, that it does a very nice job.
However, the fit Alex has shown us is based on an incorrect model equation, because @Ayushi Sharma had a typo in the first line of his post. If we substitute Alex's parameter estimates into the true model, it gives strong disagreement with the data:
load doubt
a=-54592639.7090172;
c=-31608221.6797308;
T=-1.72718650949557;
fun=@(x) a*exp(-x*T) +c*(T*(exp(-x/T)-1)+x);
plot(x,y,'o'); ax=axis;
hold on
plot( x,fun(x));
hold off
axis(ax)
legend('Data','Fit')

请先登录，再进行评论。

类别

在 Help Center 和 File Exchange 中查找有关 Get Started with Curve Fitting Toolbox 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by