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| author | ttt | 2018-12-06 13:42:14 +0530 |
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| committer | ttt | 2018-12-06 13:42:14 +0530 |
| commit | d6e8cfd86be242d0a1a09a1ef7d8b7f3d12af795 (patch) | |
| tree | fdbe9d1a10e7c256e86d7efae276fa75615cd0ba /help/en_US/schurrc.xml | |
| parent | 3ffa5ac619587eadfdb4ffd3e2fee57fee385e21 (diff) | |
| download | FOSSEE-Signal-Processing-Toolbox-d6e8cfd86be242d0a1a09a1ef7d8b7f3d12af795.tar.gz FOSSEE-Signal-Processing-Toolbox-d6e8cfd86be242d0a1a09a1ef7d8b7f3d12af795.tar.bz2 FOSSEE-Signal-Processing-Toolbox-d6e8cfd86be242d0a1a09a1ef7d8b7f3d12af795.zip | |
code by jitendra and added more test4.sce
Diffstat (limited to 'help/en_US/schurrc.xml')
| -rw-r--r-- | help/en_US/schurrc.xml | 47 |
1 files changed, 1 insertions, 46 deletions
diff --git a/help/en_US/schurrc.xml b/help/en_US/schurrc.xml index 481430a..b269840 100644 --- a/help/en_US/schurrc.xml +++ b/help/en_US/schurrc.xml @@ -17,52 +17,7 @@ <refnamediv> <refname>schurrc</refname> - <refpurpose>Computes reflection coefficients from auto-correlation sequence using Schrur algorithm.</refpurpose> + <refpurpose>narginchk(1,1,argn(2));</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - k = schurrc(r) -[k,e] = schurrc(r) - </synopsis> -</refsynopsisdiv> -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>k:</term> - <listitem><para> reflection coefficients or lattice parameters of prediction filter</para></listitem></varlistentry> - <varlistentry><term>e:</term> - <listitem><para> prediction error variance</para></listitem></varlistentry> - <varlistentry><term>r:</term> - <listitem><para>auto correltion sequence </para></listitem></varlistentry> - </variablelist> -</refsection> - - -<refsection> - <title>Description</title> - <para>k = schurrc(r) uses the Schur algorithm to compute a vector k of reflection coefficients from a vector r representing an autocorrelation sequence. k and r are the same size. The reflection coefficients represent the lattice parameters of a prediction filter for a signal with the given autocorrelation sequence, r. When r is a matrix, schurrc treats each column of r as an independent autocorrelation sequence, and produces a matrix k, the same size as r. Each column of k represents the reflection coefficients for the lattice filter for predicting the process with the corresponding autocorrelation sequence r.</para> - -<para>[k,e] = schurrc(r) also computes the scalar e, the prediction error variance. When r is a matrix, e is a column vector. The number of rows of e is the same as the number of columns of r. - -</para> -</refsection> - - - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -m=linspace(1,100); -r = xcorr(m(1:5),'unbiased'); -[k,e] = schurrc(r(5:$)) - -//EXPECTED OUTPUT -//e =1.6212406 - //k = - 0.9090909 0.2222222 0.2244898 0.2434211 - - - ]]></programlisting> -</refsection> </refentry> |