📄 tstool demo 2.htm
字号:
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
<!-- saved from url=(0049)http://www.physik3.gwdg.de/tstool/demo/demo2.html -->
<HTML><HEAD><TITLE>TSTOOL Demo 2</TITLE>
<META content="text/html; charset=gb2312" http-equiv=Content-Type>
<META content="MSHTML 5.00.2314.1000" name=GENERATOR></HEAD>
<BODY bgColor=#ffffff text=#000000 face="arial, helvetica" <font>
<H1>Example analysis of a time-series from a Colpitts oscillator</H1><BR><!--latex-off-->
<H3>Warning : This example sessions is not intended as a tutorial for nonlinear
time series analysis. It is merely meant as an introduction into the syntax of
the TSTOOL toolbox.</H3><BR><!--latex-on--><CODE>>> s =
signal('colpitts.dat','ascii')<BR>s = signal object <BR><BR>Dlens :
6001<BR>X-Axis 1 : | <BR><BR>Name : colpitts<BR>Type : <BR><BR>Attributes of
data values : <BR>| <BR><BR>Comment : <BR><BR>History : <BR>17-Aug-1999 15:08:24
: Imported from ASCII file 'colpitts.dat'<BR></CODE>
<P>By entering the above command line, the overloaded constructor for class
signal was called. Giving a filename as argument tells the constructor to load
the datafile and convert it into a signal object. The datafile 'colpitts.dat'
contains a time-series generated by an electronical oscillator that shows
nonlinear deterministic behaviour</P>
<P>To plot signal s, just issue the following command
:</P><CODE>view(s);<BR></CODE><IMG src="TSTOOL Demo 2.files/demo2a.gif">
<P>Lets find a good choice for a delay-time by using the first minimum of the
auto mutual information function</P><CODE>a =
amutual(s,32);<BR>view(a);<BR></CODE><IMG src="TSTOOL Demo 2.files/demo2b.gif">
<P>The first minimum of the auto mutual information can be found at four. Now we
need to know the minimal embedding dimension for the colpitts signal. We use
Cao's method with an delay time of four, an maximal dimension of eight, three
nearest neighbors and 1000 reference points.</P><CODE>c =
cao(s,8,4,3,1000);<BR>view(c);<BR></CODE><IMG
src="TSTOOL Demo 2.files/demo2c.gif">
<P>There's a kink in the graph produced by Cao's method at three. So now do a
time-delay reconstruction of the colpitts signal with embedding dimension 3 and
delay 4.</P><CODE>e = embed(s, 3, 4);<BR>view(e);<BR></CODE><IMG
src="TSTOOL Demo 2.files/demo2d.gif">
<P>What's the correlation dimension of the reconstructed data set ? First let's
take a look at the scaling of the correlation sum versus the radius (as log-log
plot).</P><CODE>view(corrdim2(e, -1, 0.05, 40, 32));<BR></CODE><IMG
src="TSTOOL Demo 2.files/demo2e.gif">
<P>Next, we use the Takens estimator for the correlation dimension. It needs
basically the same input arguments as the function corrdim2.</P><CODE>>>
takens_estimator(e, -1, 0.05, 40) <BR><BR>ans =<BR><BR>1.9483<BR></CODE>
<P>And what about it's largest lyapunov exponent ? To estimate the largest
lyapunov exponent, we take a look at the scaling of the prediction error.
</P><CODE>view(largelyap(e, 1000, 300, 40, 2));<BR></CODE><IMG
src="TSTOOL Demo 2.files/demo2f.gif"> <!--latex-off-->
<HR>
<IMG alt=TSTOOL border=0 src="TSTOOL Demo 2.files/logo.gif">
<P><FONT size=-1>Copyright © 1997-99 <A href="http://www.physik3.gwdg.de/">DPI
Göttingen</A></FONT> <!--latex-on--></P></BODY></HTML>
⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -