How to fit a custom equation?
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My equation is y=a(1-exp(-b(c+x)) x=[0,80,100,120,150] y=[2195,4265,4824,5143.5,5329] When I am solving it in matlab, I am not getting a proper fit in addition, sse=6.5196e+05 and r square=0.899. Although the r square value is acceptable, the sse is too high. Therefore kindly help to get minimum sse. Further I have tried in curve fitting tool but I got same thing.
Star Strider on 9 Jul 2018
I get good results with this:
yf = @(b,x) b(1).*(1-exp(-b(2)*(b(3)+x)));
B0 = [5000; 0.01; 10];
[Bm,normresm] = fminsearch(@(b) norm(y - yf(b,x)), B0);
SSE = sum((y - yf(Bm,x)).^2)
More Answers (2)
Image Analyst on 11 Jul 2018
For what it's worth, I used fitnlm() (Fit a non-linear model) because that's the function I'm more familiar with. You can see it gives the same results as Star's method in the image below. I'm attaching the full demo to determine the coefficients and plot the figure.
Alex Sha on 18 Feb 2020
If don't care the type of fitting function, try the below function, much simple but with much better result:
y = b1+b2*x^1.5+b3*x^3;
Root of Mean Square Error (RMSE): 18.0563068929128
Sum of Squared Residual: 1630.15109305524
Correlation Coef. (R): 0.999873897456616
Adjusted R-Square: 0.999495621630167
Determination Coef. (DC): 0.999747810815083
Parameter Best Estimate