How to fit complicated function with 3 fitting parameters using Least square regression
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I want to fit below equation J(v). J and V data areavailable.
N=10^21
q=1.6x10^-19
Epsilon=26.5 x 10^-14
d=3x10^- 6
Initial values may be x0=[µ l H]=[10^-5 5 10^18]
3 fitting parameters are: µ, l and H. other parameters are known.
can some one help me to solve this?
I am not expert in Matlab
V is xdata:
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
8
J is ydata:
1.64544E-05
1.99822E-05
0.000032253
4.2623E-05
7.40498E-05
0.000660899
0.007578998
0.027109725
0.106353025
0.30299725
0.7332185
1.550115
2.98009
5.3102775
8.88175
14.0394325
21.163215
댓글 수: 9
Alex Sha
2020년 3월 27일
You may try to provide a little different start-value for H, and see the final result, if final H is still alway equaling to start-value of H, your fitting seems to have problem.
채택된 답변
Alex Sha
2020년 3월 25일
Hi, you may try to use "lsqcurvefit" command or curve fitting tool box (cftool), it is also better if you post data as well as known constant values, so other persons may try for you.
추가 답변 (1개)
Jeff Miller
2020년 3월 25일
I assume you have vectors of values for V and J, in which case fminsearch might be a good choice. The basic steps are:
- Write a function "predicted" to compute a predicted value of J for any given V, µ, l and H.
- Write a function "error" that computes the sum of (predictedJ - actualJ)^2, summing across the J vector.
- call fminsearch and pass it this error function as the function to be minimized. You will have to give it reasonable guesses for µ, l and H.
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