How to fit a complex equation?

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Discussion Overview

The discussion revolves around fitting a complex equation to a set of measured data points. The equation involves parameters a, b, and m, and participants explore methods to determine appropriate values for these parameters based on the provided data.

Discussion Character

  • Exploratory
  • Mathematical reasoning
  • Technical explanation

Main Points Raised

  • One participant presents the equation Z(x) and the corresponding data points for Z at discrete values of x, seeking to identify the parameter values of a, b, and m that fit the data.
  • Another participant suggests enhancing the analysis by calculating the mean value, plotting graphs, and visually comparing data points to predict which parameter values might be closest to the observed data.
  • A third participant reiterates the equation and data, proposing a least squares fitting method to minimize the difference between the calculated and observed values of Z, while noting the potential complexity of deriving the necessary derivatives.
  • This participant also mentions the possibility of constraining m to integer values and suggests recomputing a and b based on those constraints to find the best fit.

Areas of Agreement / Disagreement

Participants express various approaches to the problem, but there is no consensus on the best method or the specific values for the parameters a, b, and m. The discussion remains open with multiple competing views on how to proceed.

Contextual Notes

Participants have not fully resolved the mathematical steps involved in the least squares fitting process, and there are assumptions regarding the integer constraint on m that may affect the outcomes.

bitele
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Z(x)=(50*m+m^2*tanh((a+b*i)*x))/(m+50*tanh((a+b*i)*x))
where i is imaginary unit, x is the independent variable, Z is the dependent variable, and a, b and m are the parameters.
We measures variable Z at a discrete set of values of variable x:
x=[1, 2, 3, 4, ...20]
Z=[57.6286+0.6328*i, 57.1207+0.6694*i, 56.9751+0.6525*i, 58.4018+0.6432*i, 57.2407+0.6809*i, 57.4864+0.6892*i, 58.7988+0.6761*i, 59.6706+0.7166*i, 58.5738+0.6758*i, 59.1651+0.6283*i, 58.4831+0.6437*i, 59.6807+0.6949*i, 58.6072+0.6453*i, 58.5499+0.6452*i, 60.0737+0.7014*i, 59.3941+0.6916*i, 58.7153+0.7090*i, 58.5133+0.6446*i, 60.8528+0.6817*i, 60.7193+0.6810*i]

We want to know what parameter values of a, b and m correspond to the data.
 
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In order to make the problem more dramatic, more elegant, you can calculate the mean value, plot a graph, add the data points with different colors, predict which one is closest, check the value of Z when subtituting data into its functon, etc etc etc
 
bitele said:
Z(x)=(50*m+m^2*tanh((a+b*i)*x))/(m+50*tanh((a+b*i)*x))
where i is imaginary unit, x is the independent variable, Z is the dependent variable, and a, b and m are the parameters.
We measures variable Z at a discrete set of values of variable x:
x=[1, 2, 3, 4, ...20]
Z=[57.6286+0.6328*i, 57.1207+0.6694*i, 56.9751+0.6525*i, 58.4018+0.6432*i, 57.2407+0.6809*i, 57.4864+0.6892*i, 58.7988+0.6761*i, 59.6706+0.7166*i, 58.5738+0.6758*i, 59.1651+0.6283*i, 58.4831+0.6437*i, 59.6807+0.6949*i, 58.6072+0.6453*i, 58.5499+0.6452*i, 60.0737+0.7014*i, 59.3941+0.6916*i, 58.7153+0.7090*i, 58.5133+0.6446*i, 60.8528+0.6817*i, 60.7193+0.6810*i]

We want to know what parameter values of a, b and m correspond to the data.

The usual method in cases like this is to do a least square fit, that is find the minimum of:

f(a,b,m)=Sum (k=1,20) |Z(k)-Zk|2
It might be messy, but you need to get the derivatives of with respect to a, b, and m. If m has to be an integer, use the integers closest to the value you get. Then recompute a and b for both cases and see which is better.
 
Hi Bitele,

The time to edit has elapsed on your current post, so just copy and paste it into a new post here and make your corrections. Just state that the new post in this thread is a correction of the original.
 

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