lattice.html
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd"><html><head> <title>Description of lattice</title> <meta name="keywords" content="lattice"> <meta name="description" content="Estimates AR(p) model parameter with lattice algorithm (Burg 1968)"> <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1"> <meta name="generator" content="m2html © 2003 Guillaume Flandin"> <meta name="robots" content="index, follow"> <link type="text/css" rel="stylesheet" href="../m2html.css"></head><body><a name="_top"></a><div><a href="../index.html">Home</a> > <a href="index.html">tsa</a> > lattice.m</div><!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png"> Master index</a></td><td align="right"><a href="index.html">Index for tsa <img alt=">" border="0" src="../right.png"></a></td></tr></table>--><h1>lattice</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>Estimates AR(p) model parameter with lattice algorithm (Burg 1968)</strong></div><h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>function [MX,PE,arg3] = lattice(Y,lc,Mode); </strong></div><h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="fragment"><pre class="comment"> Estimates AR(p) model parameter with lattice algorithm (Burg 1968) for multiple channels. If you have the NaN-tools, LATTICE.M can handle missing values (NaN), [...] = lattice(y [,Pmax [,Mode]]); [AR,RC,PE] = lattice(...); [MX,PE] = lattice(...); INPUT: y signal (one per row), can contain missing values (encoded as NaN) Pmax max. model order (default size(y,2)-1)) Mode 'BURG' (default) Burg algorithm 'GEOL' geometric lattice OUTPUT AR autoregressive model parameter RC reflection coefficients (= -PARCOR coefficients) PE remaining error variance MX transformation matrix between ARP and RC (Attention: needs O(p^2) memory) AR(:,K) = MX(:, K*(K-1)/2+(1:K)); = MX(:,sum(1:K-1)+(1:K)); RC(:,K) = MX(:,cumsum(1:K)); = MX(:,(1:K).*(2:K+1)/2); All input and output parameters are organized in rows, one row corresponds to the parameters of one channel see also <a href="acovf.html" class="code" title="function [ACF,NN] = acovf(Z,KMAX,Mode,Mode2);">ACOVF</a> <a href="acorf.html" class="code" title="function [AUTOCOV,stderr,lpq,qpval] = acorf(Z,N);">ACORF</a> <a href="ar2rc.html" class="code" title="function [MX,res,arg3] = ar2rc(ar);">AR2RC</a> <a href="rc2ar.html" class="code" title="function [MX,res,arg3,acf] = rc2ar(rc);">RC2AR</a> <a href="durlev.html" class="code" title="function [MX,res,arg3] = durlev(AutoCov);">DURLEV</a> <a href="sumskipnan.html" class="code" title="function [o,count,SSQ,S4M] = sumskipnan(i,DIM)">SUMSKIPNAN</a> REFERENCE(S): J.P. Burg, "Maximum Entropy Spectral Analysis" Proc. 37th Meeting of the Society of Exp. Geophysiscists, Oklahoma City, OK 1967 J.P. Burg, "Maximum Entropy Spectral Analysis" PhD-thesis, Dept. of Geophysics, Stanford University, Stanford, CA. 1975. P.J. Brockwell and R. A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991. S. Haykin "Adaptive Filter Theory" 3rd ed. Prentice Hall, 1996. M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981. W.S. Wei "Time Series Analysis" Addison Wesley, 1990.</pre></div><!-- crossreference --><h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>This function calls:<ul style="list-style-image:url(../matlabicon.gif)"><li><a href="sumskipnan.html" class="code" title="function [o,count,SSQ,S4M] = sumskipnan(i,DIM)">sumskipnan</a> SUMSKIPNAN adds all non-NaN values.</li></ul>This function is called by:<ul style="list-style-image:url(../matlabicon.gif)"><li><a href="demoperf.html" class="code" title="">demoperf</a> Demonstrates the much higher performance</li><li><a href="invest0.html" class="code" title="function [AutoCov,AutoCorr,MX,E,NC]=invest0(Y,Pmax,Mode);">invest0</a> First Investigation of a signal (time series) - automated part</li><li><a href="invfdemo.html" class="code" title="">invfdemo</a> invfdemo demonstrates Inverse Filtering</li><li><a href="lpc.html" class="code" title="function [A] = lpc(Y,P,mode);">lpc</a> LPC Linear prediction coefficients</li></ul><!-- crossreference --><hr><address>Generated on Tue 17-Aug-2004 00:13:21 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> © 2003</address></body></html>⌨️ 快捷键说明
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