This is  the  information sheet  about  SNNS (Stuttgart Neural Network
Simulator) and the related mailing list

        snns@informatik.uni-stuttgart.de

To  subscribe  to  this  list  mail a  message with  the  single  line
'subscribe snns <Your Full Name>' to

        listserv@informatik.uni-stuttgart.de

All  topics related to  SNNS are  welcome.  Use this list if you  have
problems  with  the  installation, questions  concerning the  provided
features, announcements of own  extensions  and tools.  We  especially
appreciate  all announcements of successful prototypes or applications
done with  SNNS, comparisons  with other  simulators,  benchmarks etc.
Also answers  to  questions  are highly  desired  as  they relieve  us
developers from this burden.

**********************************************************************
   SNNS (Stuttgart Neural Network Simulator) Version 4.0 available
**********************************************************************

The new version 4.0 of SNNS is available now.


**********************************************************************
	                New features of SNNSv4.0
**********************************************************************

Version 4.0 of SNNS features the following improvements and extensions
over the earlier version 3.3:

* A new distributed version of the kernel. SNNS can now be spread out in 
  a workstation cluster for faster learning.

* Improved version of the C-code generator snns2c

* Validation sets now can check on the performance of the network during 
  training.

* New error function in tool analyze that can handle single-output-unit
  networks. 

* New statistic tool that can predict the generalization capabilities of 
  the network.

* Reorganization of the manager panel with new selection mechanism for 
  the major SNNS windows. 

* The remote panel was alternated significantly and renamed 
  `control panel'. 

* Selection possibility in the graph panel between plot of SSE, MSE, and 
  SSE/output.

* New learning algorithm RBF_DDA

* New learning algorithm simulated annealing

* New learning algorithm Monte Carlo. 

* New learning algorithm Pruned-Cascade-Correlation

* Several new  initialization  algorithms for   Kohonen and Counter-
  propagation networks, since the two old ones had serious flaws.

* Key codes to bring up all the main panels of SNNS (e.g Alt-c for the
  control panel).

* Support for NeXT systems.

* More information  printed to the  shell during training and with the
  INFO button.

* The SOURCE and TARGET part of the info panel have been swapped for a
  more intuitive setup of the information.

* Extensive debugging (as usual).


**********************************************************************
	                      What is SNNS ?
**********************************************************************

SNNS (Stuttgart Neural Network Simulator) is a  software simulator for
neural networks  on Unix  workstations developed at the Institute  for
Parallel  and  Distributed  High  Performance  Systems (IPVR)  at  the
University of Stuttgart.  The goal of the SNNS project is to create an
efficient  and  flexible  simulation  environment for research  on and
application of neural nets.

The SNNS simulator consists of two main components:

1) simultor kernel written in C
2) graphical user interface under X11R4, X11R5, or X11R6

The simulator kernel operates  on the internal network data structures
of the neural nets and performs all operations of learning and recall.
It can also be used without the other parts as a C program embedded in
custom  applications. It supports arbitrary  network  topologies  and,
like RCS, supports the concept of  sites. SNNS can  be extended by the
user with user defined activation  functions, output  functions,  site
functions and learning  procedures, which  are  written  as  simple  C
programs and linked to the simulator kernel.

Currently the following network architectures and learning procedures
are included:

 * Backpropagation (BP) for feedforward networks
	vanilla (online) BP
	BP with momentum term and flat spot elimination
	batch BP
 * Counterpropagation, 
 * Quickprop
 * Backpercolation 1
 * RProp
 * Generalized radial basis functions (RBF)
 * ART1
 * ART2
 * ARTMAP
 * Cascade Correlation
 * Recurrent Cascade Correlation
 * Dynamic LVQ
 * Backpropagation through time (for recurrent networks)
 * Quickprop through time (for recurrent networks)
 * Self-organizing maps (Kohonen maps)
 * TDNN (time-delay networks) with Backpropagation
 * Jordan networks
 * Elman networks and extended hierarchical Elman networks
 * Associative Memory
 * RBF_DDA
 * Simulated Annealing
 * Monte Carlo. 
 * Pruned-Cascade-Correlation

A number of network pruning algorithms are available as well:
 * Optimal Brain Damage (OBD), 
 * Optimal Brain Surgeon (OBS), 
 * Skeletonization, 
 * Magnitute based pruning (Mag).

The graphical  user interface XGUI (X Graphical User Interface), built
on top of the kernel, gives  a 2D and a 3D graphical representation of
the neural networks and controls the kernel during the simulation run.
In addition, the 2D user interface  has  an integrated  network editor
which  can be used to directly create, manipulate and visualize neural
nets in various ways.


**********************************************************************
	 Machine architectures on which SNNSv4.0 is available
**********************************************************************

We  have tested  SNNSv4.0  on  the  following  machines  and operating
systems:

machine type			OS    		user interface with
 
SUN Sparc ELC, SS2, SS10	SunOS 4.1.3	X11R5, X11R6, OW 3.0  
SUN Sparc SS10, SS20		Solaris 2.3     X11R5, X11R6, OW 3.0
SGI Indigo II                   IRIX 5.1, 5.2	X11R5
DECstation 5000			Ultrix V4.2	X11R5
DEC Alpha Workstation		OSF 1.1		X11R5
IBM RS 6000/320, 320H, 520, 	AIX V3.2	X11R5
HP 9000/720, 730		HP/UX 9.0.1	X11R5
IBM-PC 80486, Pentium		Linux  		X11R5

Our parallel versions of SNNS are only available for research partners
with whom we have sponsored joint research projects. These parallel
versions include

Neurocomputer	Adaptive Solutions CNAPS serverII
SIMD computer	MasPar MP-1, MP-2
MIMD computer	Intel Paragon XP/S5, 
		Connection Machine CM-2


**********************************************************************
	          SNNSv4.0 licensing terms (short)
**********************************************************************

SNNSv4.0 is available NOW free of charge for research purposes under a
GNU-style copyright  agreement. See the license agreement in  the user
manual and in the file Readme.license of the distribution for details.
SNNS is (C) Copyright Universitaet Stuttgart, IPVR.

SNNSv4.0 can only be  obtained by anonymous ftp over the Internet. See
the detailed  description of how  to obtain SNNS below. We  don't have
the  time  and capacity  to send tapes or floppy  disks, so don't ask.
SNNSv4.0 is also too large to be  mailed  by e-mail, so don't ask  for
that,  either.  You  may,  however,  obtain  the  unmodified  SNNSv4.0
distribution from other sites  which already have obtained  it,  under
the terms  of our license agreement, if you  are  unable to connect to
our machine.

Note that SNNS has not been tested  extensively  in different computer
environments and is a research tool with frequent substantial changes.
It should be obvious that WE DO NOT GUARANTEE ANYTHING. 

We  are also not staffed  to answer problems with SNNS or to  fix bugs
quickly. For questions and/or comments concerning SNNS we refer you to 
the SNNS mailing list. To subscribe, send a mail to 
  
	listserv@informatik.uni-stuttgart.de

With the one line message (in the mail body, not in the subject)

	subscribe snns <your full name>



**********************************************************************
	              How to obtain SNNSv4.0
**********************************************************************

The SNNS simulator can be obtained via anonymous ftp from host

        ftp.informatik.uni-stuttgart.de  (129.69.211.2)

in the subdirectory        /pub/SNNS
as file
        SNNSv4.0.tar.Z			(1.99 MB)

or in several parts as files

        SNNSv4.0.tar.Z.aa  ...  SNNSv4.0.tar.Z.ae

These  split  files  are  each 400 KB and can be joined  with the Unix 
`cat' command  into one  file  SNNSv4.0.tar.Z . Be sure to set the ftp
mode to binary before  transmission  of the files.  Also watch out for
possible higher version  numbers, patches or Readme files in the above
directory  /pub/SNNS .  After successful transmission of the file move
it to the  directory  where  you want to  install SNNS, uncompress and
extract the file with the Unix commands

        uncompress SNNSv4.0.tar.Z	
        tar xvf SNNSv4.0.tar

The   SNNS  distribution  includes  full   source  code,  installation
procedures  for  supported  machine  architectures   and  some  simple
examples of trained networks.

The PostScript version of the user manual can be obtained as file 

	SNNSv4.0.Manual.ps.Z		(1.51 MB)
or 	SNNSv4.0.Manual.ps.gz		(0.95 MB)

or in 14 parts as files

        SNNSv4.0.Manual.part01.ps.Z  ...  SNNSv4.0.Manual.part14.ps.Z
	
These parts are all under 1 MB in size when uncompressed and should be
printable on any PostScript printer.Again remember to set the ftp mode 
to binary before transmission of the file(s).

There is also an Implementation Manual available as file

	SNNSv4.0.Implem.ps.Z		(0.24 MB)

and a set of extension  chapters for those users  who just printed the
manual of version 3.3 in file

	SNNSv4.0.Manual.Extensions.ps.Z		(0.30MB)

More information about SNNS as well as a html version of the manual can 
be found at 

      http://vasarely.informatik.uni-stuttgart.de/snns/snns.html

A printed version of the manual is also available. To cover for the
cost of printing and postage you should include DM 20.- for Europe, US 
$ 20.- overseas in a request for any bound manual; (this covers surface 
mail postage).


 -------------------------------------------------------------------------
|Guenter W. Mamier                      mamier@informatik.uni-stuttgart.de|
|University of Stuttgart, IPVR			                          |
|Breitwiesenstrasse 20-22, 						  |
|70565 Stuttgart, Germany                           Tel.: +49 711 7816-447|
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