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<?xml version="1.0" encoding="UTF-8"?>
<!--
*
* This help file was generated from ac2poly.sci using help_from_sci().
*
-->
<refentry version="5.0-subset Scilab" xml:id="ac2poly" xml:lang="en"
xmlns="http://docbook.org/ns/docbook"
xmlns:xlink="http://www.w3.org/1999/xlink"
xmlns:svg="http://www.w3.org/2000/svg"
xmlns:ns3="http://www.w3.org/1999/xhtml"
xmlns:mml="http://www.w3.org/1998/Math/MathML"
xmlns:scilab="http://www.scilab.org"
xmlns:db="http://docbook.org/ns/docbook">
<refnamediv>
<refname>ac2poly</refname>
<refpurpose>Convert autocorrelation sequence to polynomial of prediction filter</refpurpose>
</refnamediv>
<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
a = ac2poly(r)
[a,e] = ac2poly(r)
</synopsis>
</refsynopsisdiv>
<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>r:</term>
<listitem><para> Autocorrelation sequence to be represented with an FIR linear prediction filter</para></listitem></varlistentry>
<varlistentry><term>a:</term>
<listitem><para> Output polynomial representing the linear prediction filter e/(a(1) + a(2)z + a(3)z^2 .. a(N)z^N-1)</para></listitem></varlistentry>
<varlistentry><term>e:</term>
<listitem><para> Output scaling for the lienar prediction filter</para></listitem></varlistentry>
</variablelist>
</refsection>
<refsection>
<title>Description</title>
<para>
Function ac2poly() finds the best fit polynomial for FIR linear prediction filter a, corresponding to the autocorrelation sequence r. a is the same length as r, and is normalized with the first element. So a(1) = 1.
</para>
<para>
Author
Parthe Pandit
</para>
<para>
</para>
</refsection>
<refsection>
<title>Bibliography</title>
<para>Kay, Steven M. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice-Hall, 1988.</para>
</refsection>
</refentry>
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