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

19 vues (au cours des 30 derniers jours)
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!

Réponse acceptée

Davide Masiello
Davide Masiello le 29 Mar 2023
Modifié(e) : Torsten le 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 commentaires
ORESTE SAINT-JEAN
ORESTE SAINT-JEAN le 29 Mar 2023
Déplacé(e) : Star Strider le 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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