1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
|
<?xml version="1.0" encoding="UTF-8"?>
<!--
*
* This help file was generated from arburg.sci using help_from_sci().
*
-->
<refentry version="5.0-subset Scilab" xml:id="arburg" 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>arburg</refname>
<refpurpose>This function calculates coefficients of an autoregressive (AR) model of complex data.</refpurpose>
</refnamediv>
<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
a = arburg(x, poles)
a = arburg(x, poles, criterion)
[a, v] = arburg(...)
[a, v, k] = arburg(...)
</synopsis>
</refsynopsisdiv>
<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>x:</term>
<listitem><para> vector of real or complex numbers, of length > 2</para></listitem></varlistentry>
<varlistentry><term>poles:</term>
<listitem><para> positive integer value < length(x) - 2</para></listitem></varlistentry>
<varlistentry><term>criterion:</term>
<listitem><para> string value, takes in "AKICc", "KIC", "AICc", "AIC" and "FPE", default it not using a model-selection criterion</para></listitem></varlistentry>
<varlistentry><term>a, v, k:</term>
<listitem><para> Output variables</para></listitem></varlistentry>
</variablelist>
</refsection>
<refsection>
<title>Description</title>
<para>
This is an Octave function.
This function calculates coefficients of an autoregressive (AR) model of complex data x using the whitening lattice-filter method of Burg.
The first argument is the data sampled. The second argument is the number of poles in the model (or limit in case a criterion is supplied).
The third parameter takes in the criterion to limit the number of poles. The acceptable values are "AIC", "AKICc", "KIC", "AICc" which are based on information theory.
Output variable a is a list of P+1 autoregression coefficients.
Output variable v is the mean square of residual noise from the whitening operation of the Burg lattice filter.
Output variable k corresponds to the reflection coefficients defining the lattice-filter embodiment of the model.
</para>
</refsection>
<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
arburg([1,2,3,4,5],2)
ans =
1.00000 -1.86391 0.95710
]]></programlisting>
</refsection>
</refentry>
|
