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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 mvaar</title> <meta name="keywords" content="mvaar"> <meta name="description" content="Multivariate (Vector) adaptive AR estimation base on a multidimensional"> <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> > mvaar.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>mvaar</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>Multivariate (Vector) adaptive AR estimation base on a multidimensional</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 [x,e,Kalman,Q2] = mvaar(y,p,UC,mode,Kalman) </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"> Multivariate (Vector) adaptive AR estimation base on a multidimensional
Kalman filer algorithm. A standard VAR model (A0=I) is implemented. The
state vector is defined as X=(A1|A2...|Ap) and x=vec(X')
[x,e,Kalman,Q2] = mvaar(y,p,UC,mode,Kalman)
The standard MVAR model is defined as:
y(n)-A1(n)*y(n-1)-...-Ap(n)*y(n-p)=e(n)
The dimension of y(n) equals s
Input Parameters:
y Observed data or signal
p prescribed maximum model order (default 1)
UC update coefficient (default 0.001)
mode update method of the process noise covariance matrix 0...4 ^
correspond to S0...S4 (default 0)
Output Parameters
e prediction error of dimension s
x state vector of dimension s*s*p
Q2 measurement noise covariance matrix of dimension s x s</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)"></ul>This function is called by:<ul style="list-style-image:url(../matlabicon.gif)"></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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