RLS算法的matlab代码

%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');