<title>Neural Competitive Models Demo</title><strong><p align=right><strong>Authors: </strong><A HREF="http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/loos">Hartmut S. Loos</A><A HREF="http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/fritzke">Bernd Fritzke</A><hr></p><CENTER><!--Neural Nets-->        <H2>DemoGNG (Version 1.5)</H2>(Please wait while loading ca. 122 KByte class-code.)<p></strong></CENTER>  <i>DemoGNG</i>, a Java applet, implements several methods related to  competitive learning. It is possible to experiment with the  methods using various data distributions and observe the learning process.  A common terminology is used to make it easy to compare one method  to the other.<p><hr><center><applet archive="DemoGNG.zip" code="DemoGNG.class" width=720 height=560><param name=algorithm value="NGwCHL"><param name=distribution value="UNI"><b>Your browser does not support Java applets.</b></applet></center><p><hr><center><table border=3 width = 400><tr><td width = 0><td width = 210><center><a href="DemoGNGcode.html"><b>Java Code</b></a></center><td width = 210><center><a href="http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/JavaPaper/"><b>Model Description</b></a></center><td width = 210><center><a href="tex/DemoGNG/DemoGNG.html"><b>Manual</b></a></center><td width = 0><tr></table></center>  <p>  Hopefully, the experimentation with the models  will increase the intuitive understanding and make it easier to  judge their particular strengths and weaknesses.  <p>  The following algorithms are available:<UL><LI> LBG (Linde, Buzo, Gray)<LI> LBG-U (Fritzke)<LI> Hard Competitive Learning (standard algorithm)<LI> Neural Gas (Martinetz and Schulten)<LI> Competitive Hebbian Learning (Martinetz and Schulten)<LI> Neural Gas with Competitive Hebbian Learning (Martinetz and Schulten)<LI> Growing Neural Gas (Fritzke)<LI> Growing Neural Gas with Utility (GNG-U, Fritzke)<LI> Self-Organizing Map (Kohonen)<LI> Growing Grid (Fritzke)</UL>If you are not quite familiar with what the applet does, have a look at the<A HREF="tex/DemoGNG/DemoGNG.html"><b>manual in HTML-format</b></A>or as<A HREF="tex/DemoGNG.ps.gz"><b>Postscript version</b></A> (ca. 233 KByte).<p>For those of you, who want to go into further details:look at the technical report <A HREF="http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/JavaPaper/"><b>Some Competitive Learning Methods</b></A> by Bernd Fritzke also available as <A HREF="ftp://ftp.neuroinformatik.ruhr-uni-bochum.de/pub/software/NN/DemoGNG/sclm.ps.gz"><b>Postscript version</b></A>.Furthermore, you can<A HREF="DemoGNGcode.html#download"><b>Download the whole Package</b></A> or justhave a look at the<A HREF="DemoGNGcode.html#source"><b>DemoGNG Source</b></A> or the<A HREF="DemoGNGcode.html#cdoc"><b>Code Documentation</b></A>.