using least square fit to find the best fit parameters
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I have 3 variables y, x1, and x2 stored in vector form. I know that y=f(x1,x2) and has the form y = a*b*x1/(1+b*x1+c*x2) + d*x1. a,b,c, and d are parameters. How can I use lsqnonlin to find a,b,c,d?
Thank you
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Rik
el 26 de Feb. de 2020
You could try with a different fitting function or with different initial guesses. Those two will only help if your function properly describes the trend in your data and if your data isn't noisy in the first place.
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