lmsbianshi.m

ＬＭＳ算法用于系统辨识的MATLAB源码

clear
t=1:500
x  = 0.1*randn(1,500); % Input to the filter
       b  = fir1(31,0.5);     % FIR system to be identified
       d  = filter(b,1,x);    % Desired signal
       w0 = zeros(1,32);      % Initial filter coefficients
       mu = 0.8;              % LMS step size
       S = initlms(w0,mu);
       [y1,e1,S1] = adaptlms(x,d,S);
       
       %legend('Actual','Estimated');
       %title('System Identification of an FIR filter');grid on;
      % x  = 0.1*randn(1,500); % Input to the filter
       b  = fir1(31,0.5);     % FIR system to tbe identified
       d  = filter(b,1,x);    % Desired signal
       w0 = zeros(1,32);      % Initial filter coefficients
       P0 = 5*eye(32);        % Initial input correlation matrix inverse
       lam = 1;               % Exponential memory weighting factor
       S = initrls(w0,P0,lam);
       [y2,e2,S2] = adaptrls(x,d,S);
       figure(1)
       %stem([b.',S.coeffs.']);
       plot(t,y1,t,y2)
       figure(2)
       %stem([b.',S.coeffs.']);
      
       plot(t,e1,t,e2)
       figure(3)
        n=1:32
      stem([S1.coeffs' S2.coeffs'])
     figure(4)
      z=S1.coeffs-S2.coeffs;
     stem(n,z)
       %legend('Actual','Estimated');
      % title('System Identification of an FIR filter via RLS');grid on;