opaeumdata.rar_RLS_mathematica_数值算法/人工智能下载-pudn.com

<html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta charset="utf-8"> <meta name="generator" content="pdf2htmlEX"> <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> <link rel="stylesheet" href="https://static.pudn.com/base/css/base.min.css"> <link rel="stylesheet" href="https://static.pudn.com/base/css/fancy.min.css"> <link rel="stylesheet" href="https://static.pudn.com/prod/directory_preview_static/622b83933d2fbb00079e3bc8/raw.css"> <script src="https://static.pudn.com/base/js/compatibility.min.js"></script> <script src="https://static.pudn.com/base/js/pdf2htmlEX.min.js"></script> <script> try{ pdf2htmlEX.defaultViewer = new pdf2htmlEX.Viewer({}); }catch(e){} </script> <title></title> </head> <body> <div id="sidebar" style="display: none"> <div id="outline"> </div> </div> <div id="pf1" class="pf w0 h0" data-page-no="1"><div class="pc pc1 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="https://static.pudn.com/prod/directory_preview_static/622b83933d2fbb00079e3bc8/bg1.jpg"><div class="c x0 y1 w2 h2"><div class="t m0 x1 h3 y2 ff1 fs0 fc0 sc0 ls0 ws0">序列<span class="_ _0"> </span><span class="ff2">x(n)</span>由零均值、单位方差的白噪声<span class="_ _0"> </span><span class="ff2">w(n)</span>通过一个二阶自回归模型</div><div class="t m0 x2 h4 y3 ff2 fs0 fc0 sc0 ls0 ws0">x(n) + a</div><div class="t m0 x3 h5 y4 ff2 fs1 fc0 sc0 ls0 ws0">1</div><div class="t m0 x4 h4 y3 ff2 fs0 fc0 sc0 ls0 ws0">x(n-1) + a</div><div class="t m0 x5 h5 y4 ff2 fs1 fc0 sc0 ls0 ws0">2</div><div class="t m0 x6 h4 y3 ff2 fs0 fc0 sc0 ls0 ws0">x(n-2) =w(n)</div><div class="t m0 x7 h3 y5 ff1 fs0 fc0 sc0 ls0 ws0">产<span class="_ _1"></span>生<span class="_ _1"></span>。<span class="_ _1"></span>这里<span class="_ _1"></span>模<span class="_ _1"></span>型<span class="_ _1"></span>参<span class="_ _1"></span>数<span class="_ _2"> </span><span class="ff2">a</span></div><div class="t m0 x8 h5 y6 ff2 fs1 fc0 sc0 ls0 ws0">1</div><div class="t m0 x9 h3 y5 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _1"></span>= <span class="_ _1"></span>-1.6<span class="_ _1"></span><span class="ff1">，<span class="_ _1"></span></span>a</div><div class="t m0 xa h5 y6 ff2 fs1 fc0 sc0 ls0 ws0">2</div><div class="t m0 xb h3 y5 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _1"></span>= <span class="_ _1"></span>0.8<span class="_ _1"></span><span class="ff1">。<span class="_ _1"></span></span>LMS<span class="_ _3"> </span><span class="ff1">自<span class="_ _1"></span>适<span class="_ _1"></span>应<span class="_ _1"></span>参数<span class="_ _2"> </span></span>mu <span class="_ _1"></span>= 0.002<span class="_ _4"></span><span class="ff1">，<span class="_ _1"></span></span>RLS<span class="_ _3"> </span><span class="ff1">遗</span></div><div class="t m0 x7 h3 y7 ff1 fs0 fc0 sc0 ls0 ws0">忘因<span class="_ _1"></span>子<span class="_ _3"> </span><span class="ff2">λ<span class="_ _0"> </span></span>值<span class="_ _1"></span>选为<span class="_ _3"> </span><span class="ff3">1<span class="_ _1"></span></span>，<span class="ff2">T(-1)<span class="_ _1"></span> = 10<span class="_ _1"></span></span>。<span class="_ _1"></span>下面<span class="_ _1"></span>将仿<span class="_ _1"></span>真出<span class="_ _1"></span>权系<span class="_ _1"></span>数<span class="_ _5"> </span>逐渐<span class="_ _1"></span>收敛<span class="_ _1"></span>于<span class="_ _1"></span><span class="ff2">-1.6<span class="_ _3"> </span></span>的过</div><div class="t m0 x7 h3 y8 ff1 fs0 fc0 sc0 ls0 ws0">渡特性，分别采用<span class="_ _0"> </span><span class="ff2">LMS<span class="_ _0"> </span></span>算法和<span class="_ _0"> </span><span class="ff2">RLS<span class="_ _0"> </span></span>算法确定相应的模型参数。</div><div class="t m0 x7 h3 y9 ff4 fs0 fc0 sc0 ls0 ws0">(3) <span class="ff2">MA<span class="_ _6"></span>TLAB<span class="_ _0"> </span><span class="ff1">仿真</span></span></div><div class="t m0 x7 h3 ya ff1 fs0 fc0 sc0 ls0 ws0">图<span class="_ _0"> </span><span class="ff2">1<span class="_ _0"> </span></span>的<span class="_ _0"> </span><span class="ff2">MA<span class="_ _6"></span>TLAB<span class="_ _0"> </span><span class="ff1">源程序</span></span></div><div class="t m0 x7 h4 yb ff2 fs0 fc0 sc0 ls0 ws0">clear;</div><div class="t m0 x7 h3 yc ff2 fs0 fc0 sc0 ls0 ws0">%<span class="ff1">输入<span class="_ _0"> </span></span>x(n)</div><div class="t m0 x7 h4 yd ff2 fs0 fc0 sc0 ls0 ws0">Noise=randn(1000,1);</div><div class="t m0 x7 h4 ye ff2 fs0 fc0 sc0 ls0 ws0">x(1)=Noise(1); </div><div class="t m0 x7 h4 yf ff2 fs0 fc0 sc0 ls0 ws0">x(2)=Noise(2)+1.6*x(1);</div><div class="t m0 x7 h4 y10 ff2 fs0 fc0 sc0 ls0 ws0">for n=3:1000;</div><div class="t m0 x7 h4 y11 ff2 fs0 fc0 sc0 ls0 ws0">x(n)=Noise(n)+1.6*x(n-1)-0.8*x(n-2);</div><div class="t m0 x7 h4 y12 ff2 fs0 fc0 sc0 ls0 ws0">end</div><div class="t m0 x7 h3 y13 ff2 fs0 fc0 sc0 ls0 ws0">wL=zeros(2,1000); %<span class="ff1">权系数</span></div><div class="t m0 x7 h4 y14 ff2 fs0 fc0 sc0 ls0 ws0">wR=zeros(2,1000);</div><div class="t m0 x7 h3 y15 ff2 fs0 fc0 sc0 ls0 ws0">%<span class="ff1">期望信号<span class="_ _0"> </span></span>d(n)</div><div class="t m0 x7 h4 y16 ff2 fs0 fc0 sc0 ls0 ws0">d=x;</div><div class="t m0 x7 h4 y17 ff2 fs0 fc0 sc0 ls0 ws0">%LMS</div><div class="t m0 x7 h4 y18 ff2 fs0 fc0 sc0 ls0 ws0">for n=3:1000;</div><div class="t m0 x7 h4 y19 ff2 fs0 fc0 sc0 ls0 ws0">x1=x(n-1:-1:n-2);</div><div class="t m0 x7 h4 y1a ff2 fs0 fc0 sc0 ls0 ws0">y(n)=wL(:,n)'*x1';</div><div class="t m0 x7 h4 y1b ff2 fs0 fc0 sc0 ls0 ws0">e(n)=d(n)-y(n);</div><div class="t m0 x7 h4 y1c ff2 fs0 fc0 sc0 ls0 ws0">wL(:,n+1)=wL(:,n)+0.004*e(n)*x1';</div><div class="t m0 x7 h4 y1d ff2 fs0 fc0 sc0 ls0 ws0">end</div><div class="t m0 x7 h3 y1e ff2 fs0 fc0 sc0 ls0 ws0">%LMS<span class="_ _0"> </span><span class="ff1">权系数</span></div><div class="t m0 x7 h4 y1f ff2 fs0 fc0 sc0 ls0 ws0">a1=-wL(1,:);</div><div class="t m0 x7 h4 y20 ff2 fs0 fc0 sc0 ls0 ws0">v=[0,1000,-2.5,0];</div><div class="t m0 x7 h4 y21 ff2 fs0 fc0 sc0 ls0 ws0">axis(v);</div><div class="t m0 x7 h4 y22 ff2 fs0 fc0 sc0 ls0 ws0">n=1:1000;</div><div class="t m0 x7 h4 y23 ff2 fs0 fc0 sc0 ls0 ws0">plot(n,a1(n),'r');</div><div class="t m0 x7 h4 y24 ff2 fs0 fc0 sc0 ls0 ws0">xlabel('n');ylabel('a1(n)');</div><div class="t m0 x7 h4 y25 ff2 fs0 fc0 sc0 ls0 ws0">text(150,-0.6,'LMS');</div><div class="t m0 x7 h4 y26 ff2 fs0 fc0 sc0 ls0 ws0">text(100,-1.4,'RLS');</div><div class="t m0 x7 h4 y27 ff2 fs0 fc0 sc0 ls0 ws0">hold on;</div><div class="t m0 x7 h3 y28 ff2 fs0 fc0 sc0 ls0 ws0">yiwang=1;%<span class="ff1">遗忘因子</span></div><div class="t m0 x7 h4 y29 ff2 fs0 fc0 sc0 ls0 ws0">T=10*eye(2,2);</div><div class="t m0 x7 h4 y2a ff2 fs0 fc0 sc0 ls0 ws0">%RLS</div><div class="t m0 x7 h4 y2b ff2 fs0 fc0 sc0 ls0 ws0">for n=3:1000;</div><div class="t m0 x7 h4 y2c ff2 fs0 fc0 sc0 ls0 ws0"> x1=x(n-1:-1:n-2);</div><div class="t m0 x7 h4 y2d ff2 fs0 fc0 sc0 ls0 ws0"> k=(T*x1')/(yiwang+x1*T*x1');</div></div></div><div class="pi" data-data='{"ctm":[1.611850,0.000000,0.000000,1.611850,0.000000,0.000000]}'></div></div> </body> </html>