Fit Experimental Data with MATLAB: Best Practices

In summary, the conversation revolves around fitting experimental data with a theoretical model using MATLAB software. The suggestion is to plot the experimental data against the theoretical data to identify any discrepancies. However, the person does not have the theoretical data and is looking for ways to determine the fit parameters. A recommendation is made to run the model with input parameters matching the recorded data and use the least squares curve fit method, but the fit parameters are not constant. The person asking for help is urged to provide more details on the problem for a more accurate solution.
  • #1
sounouhid
22
2
Hello everyone
help please
I need to fit experimental data with a theoretical model using the MATLAB software what is the best way to do it?
thx for advance
 
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  • #2
Why not try plotting the experimental data vs the theoretical data to see where they differ?
 
  • #3
thank you
but i don't have the the theoretical data .i just have the model ,I'm looking for the fit parameters
 
  • #4
Can't you run the model with input parameters matching the ones recorded when you did the experiment and then plot the relevant model output?
 
  • #5
yes i did it using the least squares curve fit ,but the fit parameters are not constant they are function of the input.
 
  • #6
@ sounouhid : One cannot answer to your question without a clear description of your problem. What kind of "theoretical model" are you talking about ? What kind of data ? And more details on the context of the problem are essential. The fitting methods are numerous and depends on the context. A whole book should not large enough to answer if the problem is not restricted. For example, some procedures of fitting to several models are available in : https://fr.scribd.com/doc/14674814/Regressions-et-equations-integrales .(paper in French with some parts in English. All procedures are understandable in English)
 
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1. What is the purpose of fitting experimental data with MATLAB?

Fitting experimental data with MATLAB allows scientists to find mathematical models that best describe their experimental data. This can help in understanding the underlying relationships and patterns in the data, making predictions, and designing future experiments.

2. What are some best practices for fitting experimental data with MATLAB?

Some best practices for fitting experimental data with MATLAB include choosing appropriate models, selecting the right fitting algorithm, verifying the goodness of fit, and performing sensitivity analysis to assess the robustness of the results.

3. How do I choose the best model for my experimental data?

Choosing the best model for your experimental data involves considering the physical principles of the phenomenon being studied, the type of data being collected, and the complexity of the model. It is also important to balance between having a model that accurately describes the data and one that is simple and easy to interpret.

4. What is the role of sensitivity analysis in fitting experimental data with MATLAB?

Sensitivity analysis helps in assessing the impact of small changes in the data or model parameters on the overall fit of the model. This is important in determining the robustness of the results and identifying which parameters have the most significant impact on the model.

5. Can MATLAB fit experimental data with uncertainties?

Yes, MATLAB has built-in functions that can handle data with uncertainties. These functions allow for the inclusion of error bars or confidence intervals in the data and can perform weighted least squares fitting to account for these uncertainties.

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