readme

这个是关于neural fuzzy 算法的工具箱, 有例子程序, 请您用winzip解压缩

This distribution (v1.0, 8/31/03) contains a Matlab implementation of the 
NeuroEvolution of Augmenting Topologies (NEAT) method for evolving 
neural network topologies and weights.  The package includes
an implementation of the XOR experiment as an example.

-The complete source code in this distribution was written by Christian Mayr,
 who can be reached at:  [e-mail]

-The NEAT method was developed by Kenneth Stanley and Risto Miikkulainen.  
 Additional information and papers describing NEAT in detail can be found 
 at Ken's website: http://www.cs.utexas.edu/users/kstanley/
 A FAQ for Matlab NEAT may also become available through:
 http://www.cs.utexas.edu/users/kstanley/neat.html

This program is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License version 2 as published
by the Free Software Foundation. This program is distributed in the hope
that it will be useful, but without any warranty; without even the implied 
warranty of merchantability or fitness for a particular purpose. See the GNU 
General Public License for more details.

We hope that this software will be a useful starting point for your own
explorations in neuroevolution. The software is provided as is,
however, we will do our best to maintain it and accommodate
suggestions. If you want to be notified of future releases of the
software or have questions, comments, bug reports or suggestions
related to the software or source code itself, send
email to [e-mail].  For general questions about NEAT,
send e-mail to [e-mail], or refer to the NEAT
User's Page and FAQ at http://www.cs.utexas.edu/users/kstanley/neat.html.

NOTES:

-To run: After including the directory in which you installed matlab neat 
         in your startup.m file, just type "neat_main" at the Matlab 
         command line.  The XOR experiment will run and graphs will be 
         displayed to indicate progress.

-Data Output:  At the end of evolution, all population structures are in
         memory.  For example, to access the connection genes of population 
         member 12, you type at the prompt population(12).connectiongenes.
         The entire population is stored in the population structure.
         Matlab NEAT also outputs its structures in the file "neatsave.mat,"
         which can be loaded in using "load 'neatsave'" to restore data from 
         a prior run.  Please note that .mat files saved from Matlab NEAT
         will only be usable on the same platform on which they were saved,
         since they are saved in a binary format.
         
-Modifying the code:  It should be possible to modify the code and
         add experiments by using the extensive commenting as an aid
         in understanding the system.

-XOR Termination Criterion: XOR is considered solved if the rounded
         outputs are all correct. In other words, anything below
         0.5 is round down to 0, and anything equal or above 0.5 is rounded 
         to 1. In the case the problem is solved, Matlab NEAT automatically
         assigns a fitness of 16 in order to terminate.

-Vectorized XOR:  Since the vectorized xor evaluation is about 3-5 times 
         faster (especially with large, densely connected networks), users 
         should use this version as a template. It may look a little strange 
         (at least for users not familiar with matlab), but the speed increase 
         justifies a little more coding effort. Those more comfortable
         with the familiar can, of course, use the old (looped) version.