Nonlinear Equation Fitting: Exploring Solutions

In summary, the idea is that you can fit a model to data using non-linear least squares, but if the parameters change with x, the fit will not be accurate.
  • #1
patric44
308
40
Homework Statement
why nonlinear least square method will not work well with the function
y=kx/(5+cx)
Relevant Equations
y=kx/(5+cx)
I was trying to fit a set of data to the nonlinear equation
$$
y=\frac{kx}{5+cx}
$$
and find the parameters k,c that will result in a best fit, but (I was told without explanation) that the parameters change as we increase x, so regular fitting techniques such as nonlinear least square will not work?
can any one explain this to me, if the parameters vary as a function of the independent variable what is the best way for the fitting, and is that even possible?
 
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  • #2
The idea of fitting is based on the assumption that the parameters do NOT change with x.
Start with a plot of the data.
 
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  • #3
If there are no limits on the behavior of the parameters ##k## and ##c##, it is not only possible to fit the data, it is too easy. In that case, you could always just set ##c=1## and ##k_i = y_i \frac {5+x_i} {x_i}##. If there are no repeated ##x_i## values associated with non-equal ##y_i## values, this would give you a perfect fit. If there are some repeated values in ##x_i##s, you can just use the mean of the associated ##y_i## values. In some cases, this might give you a reasonable model, but I would not count on it.
You do not say if there is any random behavior in the data, or what the nature of the random behavior might be.
 
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  • #4
patric44 said:
I was told without explanation

Yeah...

What's the range of your ##x## ? If it's in the several thousands ##y## is a constant !

##\ ##
 
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  • #5
there is a limit on the values of k,c I suppose, they are related to some constants about the system.
the values of x is discrete like 2,4,6,... and no values in between, the values of y ranges from 100 to say 3000 and so on, what I am confused about is how its possible for a parameter to vary as a function of the independent variable like i was told?!, I mean if they really change then no fit will be accurate? not only the non linear least squares
 
  • #6
patric44 said:
what I am confused about is how its possible for a parameter to vary as a function of the independent variable like i was told?!, I mean if they really change then no fit will be accurate? not only the non linear least squares
If the parameters ##k## and ##c## change and you perform a non-linear least squares fit to determine the best CONSTANT ##k## and ##c##, then the fit will not be accurate. However, if you allow a different model, then you might get a good enough fit. Or you might try something like a cubic spline, which would fit the data in pieces with a continuous derivative. It all depends on what you want to use the model for.
 
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  • #7
FactChecker said:
If the parameters ##k## and ##c## change and you perform a non-linear least squares fit to determine the best CONSTANT ##k## and ##c##, then the fit will not be accurate. However, if you allow a different model, then you might get a good enough fit. Or you might try something like a cubic spline, which would fit the data in pieces with a continuous derivative. It all depends on what you want to use the model for.
the idea is that I want to determine the values of k,c for the specific model given by ##y=kx/(5+cx)##, because I will use the formula of the model in further calculations. from what I unstrood from you guys now I believe that the person who told me that the parameters vary could be mistaken or misinterpreted something, I really don't know
 

FAQ: Nonlinear Equation Fitting: Exploring Solutions

What is nonlinear equation fitting?

Nonlinear equation fitting is a statistical method used to model the relationship between a dependent variable and one or more independent variables when this relationship is not linear. This involves finding the best-fit curve or surface that represents the data using nonlinear functions.

Why is nonlinear equation fitting important?

Nonlinear equation fitting is crucial because many real-world phenomena are inherently nonlinear. Accurate modeling of such phenomena allows for better predictions, understanding, and optimization in various fields such as physics, biology, economics, and engineering.

What are common methods used for nonlinear equation fitting?

Common methods for nonlinear equation fitting include the Levenberg-Marquardt algorithm, gradient descent, and genetic algorithms. These methods iteratively adjust the parameters of the nonlinear model to minimize the difference between the observed data and the model's predictions.

How do you assess the goodness of fit for a nonlinear model?

The goodness of fit for a nonlinear model can be assessed using several metrics, such as the coefficient of determination (R²), root mean square error (RMSE), and residual analysis. These metrics help determine how well the model captures the variability in the data.

What are the challenges in nonlinear equation fitting?

Challenges in nonlinear equation fitting include the potential for multiple local minima, sensitivity to initial parameter estimates, and computational complexity. Properly addressing these challenges often requires careful algorithm selection, parameter initialization, and sometimes domain-specific knowledge to guide the fitting process.

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