Fitting Impedance data file to parallel RLC circuit model using fmincon
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Abdullah Qaroot
el 2 de Dic. de 2021
Comentada: Mathieu NOE
el 13 de Dic. de 2021
Hello
I am trying to fit an impedance data file from simuation to a parallel equivalent RLC circuit model. attached is files needed to run the fmincon optimaztion command.
Please any help will be appreciated.
5 comentarios
Star Strider
el 3 de Dic. de 2021
The parameter estimation or optimisation will not work with incomplete data.
I will leave you to explore this at your leisure.
Good luck, and have fun!
Respuesta aceptada
Mathieu NOE
el 3 de Dic. de 2021
hello
my attempt below - with just fminsearch (as I don't have the Optimisation Toolbox)
I refined a bit the IC by manual serach , not even sure it was needed. ...
clear all
clc
load('Y_adm.mat')
figure(1)
freq = freq*1e9; % must be GHz
Zp = 1./Yp1(:); % measured
R0 = 100;
L0 = 35e-012;
C0 = 1/(L0*(2*pi*5.85e9)^2);
x = [R0 C0 L0]; %from ADS
Zth = 1./((1./x(1)) + (1i.*2.*pi.*freq.*x(2)) - (1i./(2.*pi.*freq.*x(3))));
semilogy(freq,abs(Zp),'b',freq,abs(Zth),'r')
grid on
% curve fit using fminsearch
x = freq;
y = abs(Zp);
f = @(a,b,c,x) abs(1./((1./a) + (1i.*2.*pi.*freq.*b) - (1i./(2.*pi.*freq.*c))));
obj_fun = @(params) norm(f(params(1), params(2), params(3),x)-y);
sol = fminsearch(obj_fun, [R0,C0,L0]);
a_sol = sol(1);
b_sol = sol(2);
c_sol = sol(3);
y_fit = f(a_sol, b_sol,c_sol, x);
Rsquared = my_Rsquared_coeff(y,y_fit); % correlation coefficient
figure(2)
plot(x, y_fit, '-',x,y, 'r .', 'MarkerSize', 20)
legend('fit','data');
title(['Gaussian Fit / R² = ' num2str(Rsquared) ], 'FontSize', 15)
ylabel('Z', 'FontSize', 14)
xlabel('freq (GHz)', 'FontSize', 14)
R = a_sol
C = b_sol
L = c_sol
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function Rsquared = my_Rsquared_coeff(data,data_fit)
% R2 correlation coefficient computation
% The total sum of squares
sum_of_squares = sum((data-mean(data)).^2);
% The sum of squares of residuals, also called the residual sum of squares:
sum_of_squares_of_residuals = sum((data-data_fit).^2);
% definition of the coefficient of correlation is
Rsquared = 1 - sum_of_squares_of_residuals/sum_of_squares;
end
8 comentarios
Mathieu NOE
el 13 de Dic. de 2021
hello again
ok, I can see the result
but I am a bit lost by the fact that you have some many degrees of freedom (AB_amplitudes, AB_phase, dx, dy,...) and your only target is to get a directivity plot centered at 30°
seems like an overdetermined system to me...
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