Improve least squares solution
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I have to solve a least squares problem in which y=Ax, where y is a vector whose entries are experimental data, A is my model and x is the solution I need to find so as to weight properly my model to fit the experiments. The following figure shows in blue the experimental data (y) and in red Ax.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/192031/image.jpeg)
How could I obtain a better fit for my data in MATLAB? Is there any specific function for this? (I am not sure how to use the nonlinear least squares method, I simply solved the normal equations with the backslash \ )
3 Commenti
dpb
il 17 Lug 2018
You're apparently trying to use an extremely high-order polynomial to fit a very difficult problem.
The solution is undoubtedly to find a more suitable model.
The backslash operator is quite sophisticated despite its deceptively simple syntax; internally it does quite sophisticated stuff and generally outperforms any other technique for badly condition systems.
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