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  • Committer: Package Import Robot
  • Author(s): Sylvestre Ledru
  • Date: 2012-08-30 14:42:38 UTC
  • mfrom: (1.4.7)
  • Revision ID: package-import@ubuntu.com-20120830144238-c1y2og7dbm7m9nig
Tags: 5.4.0-beta-3-1~exp1
* New upstream release
* Update the scirenderer dep
* Get ride of libjhdf5-java dependency

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modules/signal_processing/help/ja_JP/identification/phc.xml
 
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<?xml version="1.0" encoding="UTF-8"?>
 
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<refentry xmlns="http://docbook.org/ns/docbook" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:svg="http://www.w3.org/2000/svg" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:db="http://docbook.org/ns/docbook" version="5.0-subset Scilab" xml:lang="ja" xml:id="phc">
 
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    <refnamediv>
 
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        <refname>phc</refname>
 
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        <refpurpose> Markovian表現</refpurpose>
 
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    </refnamediv>
 
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    <refsynopsisdiv>
 
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        <title>呼び出し手順</title>
 
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        <synopsis>[H,F,G]=phc(hk,d,r)</synopsis>
 
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    </refsynopsisdiv>
 
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    <refsection>
 
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        <title>パラメータs</title>
 
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        <variablelist>
 
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            <varlistentry>
 
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                <term>hk</term>
 
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                <listitem>
 
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                    <para>ハンケル行列</para>
 
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                </listitem>
 
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            </varlistentry>
 
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            <varlistentry>
 
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                <term>d</term>
 
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                <listitem>
 
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                    <para>観測量の次元</para>
 
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                </listitem>
 
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            </varlistentry>
 
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            <varlistentry>
 
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                <term>r</term>
 
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                <listitem>
 
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                    <para>
 
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                        指定する近似モデルの状態量の次元
 
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                    </para>
 
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                </listitem>
 
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            </varlistentry>
 
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            <varlistentry>
 
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                <term>H, F, G</term>
 
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                <listitem>
 
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                    <para>Markovianモデルの行列</para>
 
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                </listitem>
 
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            </varlistentry>
 
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        </variablelist>
 
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    </refsection>
 
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    <refsection>
 
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        <title>説明</title>
 
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        <para>
 
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            確率過程の共分散系列から構築されたハンケル行列から
 
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            主ハンケル要素近似法により
 
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            Markovian表現の行列<literal>H, F, G</literal>を計算する関数.
 
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        </para>
 
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    </refsection>
 
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    <refsection>
 
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        <title>例</title>
 
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        <programlisting role="example"><![CDATA[ 
 
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//This example may usefully be compared with the results from 
 
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//the 'levin' macro (see the corresponding help and example)
 
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//
 
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//We consider the process defined by two sinusoids (1Hz and 2 Hz) 
 
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//in additive Gaussian noise (this is the observation); 
 
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//the simulated process is sampled at 10 Hz.
 
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t=0:.1:100;rand('normal');
 
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y=sin(2*%pi*t)+sin(2*%pi*2*t);y=y+rand(y);plot(t,y)
 
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//covariance of y
 
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nlag=128;
 
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c=corr(y,nlag);
 
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//hankel matrix from the covariance sequence
 
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//(we can choose to take more information from covariance
 
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//by taking greater n and m; try it to compare the results !
 
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n=20;m=20;
 
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h=hank(n,m,c);
 
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//compute the Markov representation (mh,mf,mg)
 
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//We just take here a state dimension equal to 4 :
 
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//this is the rather difficult problem of estimating the order !
 
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//Try varying ns ! 
 
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//(the observation dimension is here equal to one)
 
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ns=4;
 
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[mh,mf,mg]=phc(h,1,ns);
 
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//verify that the spectrum of mf contains the 
 
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//frequency spectrum of the observed process y
 
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//(remember that y is sampled -in our example 
 
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//at 10Hz (T=0.1s) so that we need 
 
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//to retrieve the original frequencies through the log 
 
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//and correct scaling by the frequency sampling)
 
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s=spec(mf);s=log(s);
 
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s=s/2/%pi/.1;
 
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//now we get the estimated spectrum
 
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imag(s),
 
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 ]]></programlisting>
 
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    </refsection>
 
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    <refsection role="see also">
 
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        <title>参照</title>
 
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        <simplelist type="inline">
 
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            <member>
 
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                <link linkend="levin">levin</link>
 
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            </member>
 
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        </simplelist>
 
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    </refsection>
 
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</refentry>