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- function [] = mcmmp_oe2(A, B, N, nr)
- lambda = 1;
- % Verificarea corectitudinii parametrilor de apel ai rutinei
- if(nargin < 4)
- nr = 100;
- end
- % Generare de date
- u = sign(randn(N, nr));
- e = randn(N, nr) * lambda^2;
- yi = filter (B,A, u);
- v = e;
- y = yi + v;
- y2 = e - y;
- teta = zeros(4, nr);
- % Aplicare metoda MCMMP
- for i=1:nr
- % Ry2
- [ry2, k] = xcov(y2(:, i),'biased');
- ry2 = ry2(k>=0);
- % Ru
- [ru, k] = xcov(u(:, i),'biased');
- ru = ru(k>=0);
- % Ryu
- [ryu, k] = xcov(y(:, i), u(:,i), 'biased');
- ryu = ryu(k>=1);
- % Ry2u
- [ry2u, k] = xcov(y2(:, i), u(:,i), 'biased');
- ry2u = ry2u(k>=0);
- % Ruy2
- [ruy2, k] = xcov(u(:, i), y2(:,i), 'biased');
- ruy2 = ruy2(k>=0);
- % Ryy2
- [ryy2, k] = xcov(y(:, i), y2(:,i), 'biased');
- ryy2 = ryy2(k>=-1);
- % Cacul matrice de covarianta a datelor (R)
- R = [ry2(1) ry2(2) ry2u(1) ry2u(2);
- ry2(2) ry2(1) ruy2(2) ry2u(1);
- ruy2(1) ruy2(2) ru(1) ru(2);
- ry2u(2) ruy2(1) ru(2) ru(1)];
- % Calcul vector de covarianta a datelor (r)
- r = [ryy2(3);
- ryy2(4);
- ryu(1);
- ryu(2)];
- % Estimare parametrii pentru realizarea curenta
- t = R \ r;
- teta(:, i) = t;
- % Estimare zgomot alb si dispersie
- % La eroare avem matrice Toeplitz
- eroare = y(:,i) - [[0; y2(1:(N-1),i)] [0; 0; y2(1:(N-2),i)] [0; u(1:(N-1),i)] [0; 0; u(1:(N-2), i)]]*t;
- deviatiastandard(i, 1) = norm(eroare)/sqrt(N-size(teta,1));
- dispersia(i,1) = deviatiastandard(i)^2;
- end
- % Calcul medie parametrii estimati
- a1_m = mean(teta(1,:));
- a2_m = mean(teta(2,:));
- b1_m = mean(teta(3,:));
- b2_m = mean(teta(4,:));
- % Afisare grafic
- figure
- plot(dispersia)
- hold on
- plot(zeros(nr, 1) + lambda^2);
- hold off
- title('Model OE[2, 2]');
- xlabel('Numarul realizarii');
- ylabel('Varianta');
- legend('zgomot estimat', 'zgomot real');
- text(2, 1.2, ['Parametrii adevarati a2 a3 b2 b3: ', sprintf('%9.4f',[A(2) A(3) B(2) B(3)])]);
- text(2, 0.8, ['Parametrii estimati a1m a2m b1m b2m:', sprintf('%9.4f',[a1_m a2_m b1_m b2_m])]);
- end
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