Hi all, im searching a way to optimize my objective function (RMSE) by using fminsearch. I have 4 variables: a, b, c & d.
Also, I have 23 simple equations that have to be replaced with the values a, b, c and d (these are the values to optimize).
Then a 1x23 column is created which I call CFPPi with the results of these values.
The next instruction is to create a 23 x69 matrix with the following instruction: prediction = CFPPi. * biodiesel_composition (biodiesel_composition is a 23x69 database).
Then I create a 1 x 69 row by doing the sum of each prediction column using the following operation biodisel_cfpp = sum (prediction, 1).
Lastly, I have another row of 1 x69, called experimental_value. These are the values that I want to approach by optimizing a, b, c and d and these 4 variables could be any random number that helps to minimize the error.
The main purpose is to make the RMSE error as small as possible. I did the above on Excel' solver using GNR nonlinear method and the results were pretty good but i want to double check in matlab if the optimization could be better. Can anyone suggest a solution?
Thanks in advance for your valuable help :)
syms a b c d
function rmse (experimental_values,biodisel_cfpp)
r = sqrt(sum((experimental_values(:)-biodisel_cfpp(:)).^2/numel(experimental_values)))