RLS递归最小二乘(最新整理)

1、实验二：实现自适应的递归最小二乘法(rls)一、实验目的利用 matlab 实现自适应的递归最小二乘法(rls)二、实验过程首先掌握 rls 算法原理；然后利用 matlab 实现；最后得出结果；三、实验程序%rls 算法randn(seed,0):%seed 相当于知名了产生随机数的一个起始点rand(seed,0);noofdata=8000;%设定训练数列点的个数order=32;%设置自适应滤波器lambda=0.98;%设置遗忘因子delta=0.001;%对于 delta 的初始化x=randn(noofdata,1);%假设输入为白噪声h=rand(order,1);%系统随机选

2、择d=filter(h,1,x);%产生输出%rls 初始化p=delta*eye(order,order); w=zeros(order,1);%rls 自适应for n=order:noofdata; u=x(n:-1:n-order+1); pi_=u*p; k=lambda+pi_*u; k=pi_/k;e(n)=d(n)-w*u; w=w+k*e(n); pprime=k*pi_;p=(p-pprime)/lambda; w_err(n)=norm(h-w);end%画出结果figure(1)plot(20*log10(abs(e); title(learning curve);xl

3、abel(itergation number);%迭代次数ylabel(output estimation error in db);figure(2) semilogy(w_err);title(weight estimation error); xlabel(itergation number); ylabel(weight error in db);四、实验结果“”“”at the end, xiao bian gives you a passage. minand once said, people who learn to learn are very happy people. i

4、n every wonderful life, learning is an eternal theme. as a professional clerical and teaching position, i understand the importance of continuous learning, life is diligent, nothing can be gained, only continuous learning can achieve better self. only by constantly learning and mastering the latest relevant knowledge, can employees from all walks of life keep up with the pace of enterprise development and innovate to meet the needs of the market. this document is also edited by my studio professionals, there may