# Neural network for curve fitting (estimating function parameters)

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Hi, I have a question regarding Neural network fitting.

I have curves where I know the curve prescription, but I don't know the parameters (let's say, for example, the curve prescription will be y = a*x^2+b*x+c), and there is some noise in these data.

I need to estimate the parameters a,b,c and the way I am currently doing it is through minimizing square error through fminsearch, but it sometimes has trouble fitting the curve properly (my real data are quite noisy and the curve has 6 parameters). So I wanted to try a different approach and Neural fitting looks quite promising, but I have never done anything with neural networks, so I wanted to ask, if it could be possible to use it for estimating the parameters of a curve (I have a lot of simulated data, where I know the parameters and it could be used for training). Also, in Neural network fitting app, as a input, should I use values of y as input and values of a,b,c as target?

Thank you

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### Answers (1)

Bjorn Gustavsson
on 6 Feb 2023

In any type of parameter fitting problem you're facing the curse of dimensionality in one form or the other, because the number of dimensions in your search-space increase with the number of parameters. To evaluate your error-function for all combinations of three values per parameter goes from 3 for 1 parameter, to 9 for 2 and 729 for 6 parameters. In addition the optimization will typically have a more complex shape of the error-function to search through, often with different regions of attraction to different local minima. Therefore the search becomes increasingly difficuly the larger number of parameters you fit. One additional stumbling-stone is if your parameters have one redundant parameter (or nearly redundant), for example a model-function of the form:

(only for illustrating purposes) where the coefficient for the linear term has a redundant parameter, this often makes the optimization waste time trying to find optimal values for these two parameters in vain.

Trying to solve these types of issues by turning to a neural network appears to me to be turning to one "more general black-box" machine instead of the black-box machines designed to solve these types of problems, that seems the wrong way to go.

My suggestion is instead that you should try to use a more considered aproach to the fitting:

1, make sure your parameters are truly non-redundant. (this is a pratfall that stumped me a couple of times before I realized I couldn't be that lazy).

2, Try to constrain the search-space, positivity-constraints, range of any of the parameters, anything that helps reducing the parameter-space should help. If you don't have the optimization toolbox you can still use:

3, try to turn to lsqnonlin instead of fminsearch, it often is more efficient. The only thing you need to change is to convert your error-function from returning the sum of squared residuals to a residual-function returning the individual residuals.

4, Definitely try multi-start optimization, this to avoid getting trapped in a local minima.

HTH

##### 2 Comments

Bjorn Gustavsson
on 7 Feb 2023

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