rlsxxx.m

本代码比较了在四种不同特征值扩散度情况下RLS算法的学习曲线

%本例比较了在四种特征值扩散度不同的情况下RLS算法的学习曲线
clear all
close all
sigma = 0.001;                 %噪声功率
NR = 11;                        %抽头数
N = 300;                        %采样次数
wn=[3.5 3.3 3.1 2.9];           %设置四种特征值扩散度
E=zeros(N-NR+1,4);              %误差矩阵的初始化
for t = 1:4
    W = wn(t);
    %独立的200次实验
    for j=1:200       
        %加入信道的随机序列
        X = binornd(1, 0.5, N, 1)*2 - ones(N, 1);  
        %信道冲激响应
        n=1:3;
        Hn = 1/2 * (1+ cos(2 * pi / W * (n-2)));
        
        C = conv(X, Hn);
        %信道噪声为加性高斯白噪声
        B = normrnd(0, sqrt(sigma), length(C), 1);
        C = B + C;
        %算法的初如化设置
        delta = 0.004;
        lambda = 1.0;
        Mn = zeros( NR, 1);
        Pn = 1/delta * eye(NR);
        
        for i =1: N-NR+1 
            Xn = C(i:i+NR-1,1);
            Pin = Pn * Xn;
            Kn = Pin./(lambda + Xn' * Pin);
            Y(i) = Mn' * Xn;
            e(i) = X(i+7) - Y(i);
            Mn = Mn + Kn * e(i);
            Pn = 1/lambda * Pn-1/lambda * Kn * Xn' * Pn;
            E(i,t) = E(i,t)+e(i)^2;            %每种特征值下的集平均误差
        end
    end
end
semilogy(E/200);legend('W=3.5','W=3.3','W=3.1','W=2.9');