Bi-exponential function fitting in Origin Lab SW

In summary, the conversation discusses a set of data and the use of an exponential function fit to analyze it. However, the first set of data produces an error due to over-parameterization of the model. The person also mentions having similar sets of data and the need to use the function in the form y = A1*exp(-x/t1) + A2*exp(-x/t2) + y0. They also provide the specific values of the parameters for the good set of data for comparison.
  • #1
Jakub
5
0
I can't understand the exponential function fit for this set of data works well:
ExpDec2 exponential function fit
0 3,04
10 2,77
20 2,52
30 2,27
40 2,09
50 1,92
60 1,75
70 1,62
80 1,51
90 1,43
100 1,36
110 1,29
120 1,24
130 1,19
140 1,14
150 1,09
160 1,05
170 1,02
180 0,99
190 0,97
200 0,95
210 0,93
220 0,91
230 0,89
240 0,87
250 0,85
260 0,83
270 0,82
280 0,8
290 0,79
300 0,78
310 0,77
320 0,73
330 0,71
340 0,7
350 0,69
360 0,67
370 0,66
380 0,64
390 0,63
400 0,62
410 0,61
420 0,63
430 0,59
440 0,57
450 0,56
460 0,55
470 0,54
480 0,54
490 0,53
500 0,52
510 0,52
520 0,51
530 0,5
532,5 0,5

But this set with the same settings gives me the following error:
0 4,1
10 3,63
20 3,29
30 3,05
40 2,74
50 2,51
60 2,29
70 2,07
80 1,91
90 1,75
100 1,62
110 1,52
120 1,44
130 1,37
140 1,3
150 1,26
160 1,21
170 1,16
180 1,13
190 1,08
200 1,04
210 1,01
220 0,98
230 0,96
240 0,93
250 0,92
260 0,9
270 0,89
280 0,87
290 0,86
300 0,85
310 0,84
320 0,83
330 0,83
340 0,82
350 0,81
360 0,8
370 0,79
380 0,78
390 0,77
400 0,76
410 0,75
420 0,73
424 0,73
Fit did not converge, because mutual dependency exists between parameters. The model is over-parameterized, so the fitter cannot find a fixed parameter value. Try simplifying the function, or fixing several parameter values.

I have several similar sets of data, this one is the only one that makes problem. This is confusing. Any help pls
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  • #2
I can't really change the type of function since I need it in the form y = A1*exp(-x/t1) + A2*exp(-x/t2) + y0

The one that did not converge has too big bonds
y0 -21656,44468 ± 215638,46693
A1 3,1442 ± 0,02977
t1 70,10402 ± 1,29334
A2 21657,39982 ± 215638,46692
t2 4,32545E7 ± 4,30752E8

To compare here is the good one
y0 0,78075 ± 0,01839
A1 0,68676 ± 0,07776
t1 9,97927 ± 1,98029
A2 3,51012 ± 0,06242
t2 86,08062 ± 2,56822
 

What is Bi-exponential function fitting in Origin Lab SW?

Bi-exponential function fitting is a statistical method used in Origin Lab software to describe data that follows a bi-exponential growth or decay pattern. It involves fitting a curve to the data points using a mathematical function with two exponential terms.

Why is Bi-exponential function fitting important?

Bi-exponential function fitting is important because it allows researchers to accurately model and analyze data that follows a bi-exponential trend. This type of data is commonly seen in biological and chemical processes, and understanding the underlying pattern can provide valuable insights and predictions.

How is Bi-exponential function fitting performed in Origin Lab SW?

In Origin Lab software, Bi-exponential function fitting is performed by selecting the appropriate function (e.g. Bi-Exponential Rise or Bi-Exponential Decay) and adjusting the parameters to best fit the data. The software uses a curve fitting algorithm to find the best fit and provides a statistical report of the fit quality.

What are the advantages of using Bi-exponential function fitting in Origin Lab SW?

There are several advantages to using Bi-exponential function fitting in Origin Lab software. It allows for more accurate and precise analysis of data, especially when the data follows a bi-exponential trend. Additionally, the software provides a visual representation of the fit and statistical measures of its quality, making it easier to interpret the results.

Are there any limitations to Bi-exponential function fitting in Origin Lab SW?

While Bi-exponential function fitting is a powerful tool, it does have some limitations. It may not be suitable for all types of data and may not provide an accurate fit if the data does not follow a bi-exponential trend. It also relies on the user to select the appropriate function and adjust the parameters, which may introduce bias in the results.

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