Hello,
Using this fit formula from below, how to determine a, b, c, d, e, and x coefficients in such a way to fit the below plot Flux Density vs Magnetizing Force:
I think (but I do not know how to do) to read all the data points of each line (60uH, 40uH, 26uH) and then to make the curve fitting by using genfit Mathcad function, minerr or something like that.
ACCEPTED SOLUTION
Accepted Solutions
Do you really want a separate fit for each of the three curves?
Or is one of the 6 parameters a,b,c,d,e,x dependent of the inductivity value (60 µH, 40 µH, 26 µH) and if yes, in which way. You could then do the very same as I have shown in your other current curve fit question.
But comparing the results for all three inductivities shows that all six parameters are completely different and so may all be dependent in some way of the inductivity value.
Furthermore you get quite different results for the same data set with different guess values.
All seem to provide a fairly good fit (B4 fails for lower H values, though).
For example below are the data points for the 3 lines: 60uH, 40uH, and 26uH lines from above graph.
MCP10 file attached.
Only x axis is log scale.
Now we need to find the values of a, b, c, d, e, and x coefficients in such a way to be able to generate these lines also with the above formula by fitting these curve with that given formula, by using genfit/mineer or something similar mathcad functions.
Do you really want a separate fit for each of the three curves?
Or is one of the 6 parameters a,b,c,d,e,x dependent of the inductivity value (60 µH, 40 µH, 26 µH) and if yes, in which way. You could then do the very same as I have shown in your other current curve fit question.
But comparing the results for all three inductivities shows that all six parameters are completely different and so may all be dependent in some way of the inductivity value.
Furthermore you get quite different results for the same data set with different guess values.
All seem to provide a fairly good fit (B4 fails for lower H values, though).
I am not aware of any relationship between any of a,b,c,d,e,x parameters and inductivity values. So, we need to go with no dependency between any of parameters. But I understand that if no dependency is assumed between one of a, b, c, d, e or x parameters and inductivity values then we need to make 3 genfit/Minerr fit curve separately for each inductivity values (60 µH, 40 µH, 26 µH), right?
This below variant is with Minerr, fit curve for 26uH inductivity value.
fit curve for 60uH inductivity value.
And also for the last fit curve of 40uH, as above.
Is better Minerr than genfit?
But I understand that if no dependency is assumed between one of a, b, c, d, e or x parameters and inductivity values then we need to make 3 genfit/Minerr fit curve separately for each inductivity values (60 µH, 40 µH, 26 µH), right?
Right. If the equation does not contain a variable for these values, you can't expect one single equation (dependent on that variable) to cover all possible inductivity values like we could do in your other question with the frequency f.
Is better Minerr than genfit?
Don't think so. Usually the result are the same. Minerr may have advantages if you intend to apply additional constraints like a>5 or the like. You can't do so with genfit.
But as I had shown above, there seem to be a lot of possible functions of the type you provide with much different parameter values which all yield more or less suitable fits. The results depend heavily on he guess values provided. This is because the function type you had chosen depends on a lot (six) independent parameters.
Why must it be this specific function type?
In a non-log plot the correlation looks almost linear!?
Here is the linear fit - looks pretty good to me.
Unfortunately we only have data for three different inductivity values, so its hard to guess in which way slope and intersection may depend on the inductivity.
Slope may be linear ?
@Cornel wrote:
@Werner_E wrote:
Why must it be this specific function type?This is the fit formula given by the manufacturer of the inductor.
@Werner_E wrote:In a non-log plot the correlation looks almost linear!?
Manufacturer gives the plots only with log plot, not with non-log plot. Thus I cannot say anything about non-log plot correlation.
You sure can say a lot about the non-log plot. You have the data, can easily create that kind of plot and can look at it and see that it seems to be close to linear functions.
According the function type given by the manufacturer: It actually includes the linear function f26(H)=k26*H+d26 !!
Simply set x=1, c=d=e=0, b=k26 and a=d26 and you get that linear function.
We can "refine" the values by using a solve block with minerr (genfit fails here for reasons unknown to me, so the solve block seems to be more stable). The function B26) which we get that way has as slightly better fit (correlation slightly close to 1), but you have to decide if that's worth the effort. After all having a simple linear function can be of benefit, too.
Using the linear approach we could try to develop a general function valid for any inductivity L
