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Feat generar paquete #4

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Feat generar paquete #4

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6095813
docs
Jun 25, 2020
bf63d95
generar paquetes
kps-dot Jun 25, 2020
1e00abc
Merge branch 'feat_generar_paquete' of https://github.com/acesrg/iden…
kps-dot Jun 25, 2020
2937170
modificacion generar paquetes
kps-dot Jul 1, 2020
c93a1bd
:sparkles: se agrega .gitignore
paulaolmedo Jun 25, 2020
a8403ad
:sparkles: generar paquetes de identificacion
Jul 1, 2020
2b50812
Jul 1, 2020
35501d7
agregue la suma de un ruido blanco en la función add_white_noise_to_func
eduardoadam Jun 25, 2020
2137420
:sparkles: se arma paquete
kps-dot Jul 1, 2020
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2 changes: 2 additions & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
@@ -0,0 +1,2 @@
#Archivos temporales de matlab
*.m~
26 changes: 23 additions & 3 deletions identification_methods.m
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Expand Up @@ -36,7 +36,6 @@
%% Identifico por minimos cuadrados en forma recursiva
plot_ident = true;
Gzi_mc = discrete_ident_recursive_least_squares(data_ident, Ts, plot_ident)

%% Validacion de resultados
validate_identifications(data_test, Gzi, Gzi_mc)

Expand All @@ -50,6 +49,7 @@
%# OUTPUT noisy (simbólico) señal con ruido agregado
%#

noiseSignal = awgn (clean_signal, snr)

end

Expand All @@ -61,7 +61,11 @@
%# INPUT output_signal (double-sym) la señal de salida
%# INPUT sample_time (double) tiempo de muestreo
%# OUTPUT [data_ident(iddata), data_validation(iddata)]

% Armo el paquete de datos
N1 = floor(N/2);
data = iddata(y, u, Ts);
data_ident = data(1:N1);
data_test = data(N1+1:N);
end

function Gzi = discrete_ident_arx(data, Ts, focus_mode, na, nb, nk, residual_analysis)
Expand All @@ -88,7 +92,22 @@
%# INPUT Ts(double): tiempo de muestreo
%# INPUT plot_ident(logical): opción de plotear la identificación
%# OUTPUT Gzi_mc(tf): función de transferencia identificada


N1=plot_ident;
%prueba
n = 3;
u = data.InputData;
y = data.OutputData;
Theta = zeros(4, N1);
P = 1e12*eye(4);

for k = n:N1-1
Phi = [-y(k-1) -y(k-2) u(k-1) u(k-2)];
K = P*Phi'/(1+(Phi*P*Phi'));
Theta(:,k+1) = Theta(:,k)+K*(y(k)-Phi*Theta(:,k));
P = P-(K*Phi*P);
end
Gzi_mc = tf(Theta(3:4,N1)', [1 Theta(1:2,N1)'], Ts)
end

function analyze_residuals(data, sys_id, sampling_frequency)
Expand All @@ -101,6 +120,7 @@ function analyze_residuals(data, sys_id, sampling_frequency)

end


function validate_identifications(data, Gzi, Gzi_mc)
%#VALIDATE_IDENTIFICATIONS
%#
Expand Down