Perhaps simple question, but I (the simple) need some guidance. The following applies to a project ongoing and is a challenge in that I am not a math whiz!
As example, I wish to measure temperature through a sensor whose resistance changes with temperature. It is a non-linear sensor. It's easy to create a calibration table which provides two columns, the Actual Temperature and the Resistance Result. As I am interested in 0-10 Degrees C, my table might consist of three rows:
Temperature Resistance
__________________________
0 Degrees C 100.4 Ohms
5 Degrees C 110.8 Ohms
10 Degrees C 140.2 Ohms
So I have taken three measurements at the bottom, middle, and top of my range.
Now, if I were to plot this, we'd see quite a jump between 5 and 10 C, as the device is not linear.
If under actual conditions I then read 102.0 Ohms, what might the best method be to determine the Temperature at the given resistance? What about at 115.3 Ohms? Same method?
Of course, I could say that between 0 and 5 C we have 2.08 Ohms per Degree C, and between 5 and 10 we have 5.88 Ohms per Degree C. I bet there's a better method that will fit to a curve.
As the higher range is wild, I might have to take a few more calibration steps between these values to get more data. Seems reasonable. But what then? Must I store this table and apply a Ohms per Degree factor if the value is within a particular calibration step range?
Maybe this a good place for the use of a polynomial equation, but I am a bit green here. Please help with an example as I best learn by that method. I'm hoping for the best "curve fit" without too much math processing overhead.
Thank you!
You could look into polynomial fitting methods, but they will each have their own set of problems.
This could be made worse by more data points, by nonlinear behaviors and a host of other things, so you'll get different mileage.
For the particular example you gave, I used Wolfram Alpha and it provided two approaches that include a linear and periodic approach.
If it could have done a least-squares fit, it would have provided that too.
So you can see, not too good. You could try adding another handful of data points and see if it improves things.
I am surprised you can not get the data sheet and see if they provide a temperature based model for this sort of thing.