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Minimize the sum of squared errors between the experimental and predicted data in order to calculate two parameters

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In my research work, I use a model and I want to minimize the sum of squared errors between the experimental and predicted data in order to calculate two parameters.
The experimental data are:
u exp: [0.709; 0.773 ;0.823 ;0.849 ;0.884 ;0.927 ;0.981 ;1.026 ;1.054 ;1.053 ;1.048;1.039] ;
observed at z=[ 0.006;0.012;0.018;0.024;0.03;0.046;0.069;0.091;0.122;0.137;0.152;0.162];
The equation of the model that I use is:
u model=0.1073*((log(0.13/z)-1/3*(1-(z/0.13)^3)+2*a*(1+(b)^0.5)*cos(11.89*z)); and I want to calculate the parameters “a” et “b” by minimizing the sum of squared errors between “u exp” and “u model”.
Someone here can help me please?
Thank you already for your help!

Accepted Answer

Davide Masiello
Davide Masiello on 29 Mar 2023
Edited: Torsten on 29 Mar 2023
You can use MatLab's fmincon.
z = [0.006;0.012;0.018;0.024;0.03;0.046;0.069;0.091;0.122;0.137;0.152;0.162];
u_exp = [0.709;0.773;0.823;0.849;0.884;0.927;0.981;1.026;1.054;1.053;1.048;1.039];
u_mod = @(P) 0.1073*(log(0.13./z)-1/3*(1-(z/0.13).^3)+2*P(1).*(1+P(2).^0.5).*cos(11.89*z));
sum_sq_err = @(P) sum((u_exp-u_mod(P)).^2);
P = fmincon(sum_sq_err,[0.1,0.1]);
Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.
a = P(1)
a = 2.0158
b = P(2)
b = 0.3185
hold on
plot(z,u_exp,'o')
plot(z,u_mod(P))
hold off
grid on
  4 Comments
ORESTE SAINT-JEAN
ORESTE SAINT-JEAN on 29 Mar 2023
Moved: Star Strider on 29 Mar 2023
z = [0.006;0.012;0.018;0.024;0.03;0.046;0.069;0.091;0.122;0.137;0.152;0.162];
u_exp = [0.345;0.281;0.231;0.205;0.17;0.127;0.073;0.028;0.00;0.0010;0.0060;0.015];
u_mod = @(P) 0.1073*((log(0.132./z)-(1/3)*(1-(z/0.132).^3)+2*P(2).*(1+(P(1)).^0.5).*(cos(pi*z/0.264)).^2));
sum_sq_err = @(P) sum((u_exp-u_mod(P)).^2);
P = fminsearch(sum_sq_err,[0.01,0.01])
hold on
plot(z,u_exp,'o')
plot(z,u_mod(P))
hold off
grid on
P =
0.0506 0.2198
With the god data, everything it's ok.
THANK YOU FOR YOUR HELPS!!

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