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  <refnamediv>
    <refname>poly2ac</refname>
    <refpurpose>Convert prediction polynomial to autocorrelation sequence.</refpurpose>
  </refnamediv>


<refsynopsisdiv>
   <title>Syntax</title>
   <synopsis>
   R = poly2ac(a,efinal)
   
   </synopsis>
</refsynopsisdiv>

<refsection>
   <title>Parameters</title>
   <variablelist>
        <varlistentry>
            <term>a:</term>
            <listitem><para> input prediction polynomial with 1st element 1 (if not, poly2ac normalizes it to 1 before proceeding).</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>efinal:</term>
            <listitem><para> input prediction error</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>r:</term>
            <listitem><para> output autocorrelation sequence</para></listitem>
        </varlistentry>
   </variablelist>
</refsection>

<refsection>
   <title>Description</title>
   <para>
This function obtains the underlying autocorrelation sequence that would best fit a linear prediction filter described by the
denominator polynomial and the numerator scaling. The filter is H(z) = efinal/(a(1) + a(2) x z a(3) x z^2 ... a(n) x z^n-1)
   </para>
   <para>
</para>
</refsection>

<refsection>
   <title>Examples</title>
   <programlisting role="example"><![CDATA[
a = [1.0000 0.4288 0.76 0.0404 -0.02]
efinal = 0.2           // Step prediction error
r = poly2ac(a,efinal)   // Autocorrelation sequence

   ]]></programlisting>
</refsection>

<refsection role="see also">
   <title>See also</title>
   <simplelist type="inline">
   <member><link linkend="ac2poly">ac2poly</link></member>
   <member><link linkend="poly2rc">poly2rc</link></member>
   <member><link linkend="rc2poly">rc2poly</link></member>
   <member><link linkend="rc2ac">rc2ac</link></member>
   <member><link linkend="ac2rc">ac2rc</link></member>
   </simplelist>
</refsection>

<refsection>
   <title>Authors</title>
   <simplelist type="vert">
   <member>Parthe Pandit</member>
   </simplelist>
</refsection>

<refsection>
   <title>Bibliography</title>
   <para>S. Kay, Modern Spectral Estimation, Prentice Hall, N.J., 1987, Chapter 6.</para>
</refsection>
</refentry>
