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Synworks 2.1 available for FTP
SynWorks - A design, training, test, and visualization environment for
2.1, September 1993
Rintheimer Strasse 59
D 76131 Karlsruhe, FRG
Phone : (+49) (721) 61 18 19
EMail : firstname.lastname@example.org
SynWorks is a fully integrated environment for design,
training and test of neural networks. Currently, it supports
11 different network types, the networks' sizes are only
limited by the amount of memory available.
SynWorks provides instruments to watch and track (to disk)
all important network parameters, all kinds of errors and
of course, all input and output values provided to or by
All network parameters can be edited, and, starting from
standard networks, custom networks can be created by
adding links and neurons, modifying all functions (learn,
transfer, error, evaluation). Input/output behaviour can
easily be specified in terms of ranges, sources, and
targets. Network display modes include structural,
error-related, change-related, weight-related and
memory related displays. All displays provide point-
and click interfaces to the displayed parameters and
are available simultaneously (watch chipmem !).
All networks can be printed in four different
modes, giving a textual, structural and two different
descriptions on the network's weight map.
SynWorks also provides a context-sensitive on-line help
system that includes descriptions of all actions and
important components, linked together via cross-references
and easily accessible via buttons and hot-keys.
Also, SynWorks features a full AREXX-interface that allows
to define macros for internal use as well as using a network
as part of a larger system, execute demonstrations etc..
The full version of SynWorks includes five disks with
both a 68000 and a 68020/881+ version of SynWorks, several
examples, linkable libraries for easy use of networks in
other applications (supporting the Amiga and Sun
workstations), a tool for data visualization and
a set of printed manuals, including a short introduction
to neural networks.
With SynWorks, people generally interested in neural networks
are provided with an easy-to-handle, integrated environment
that allows them to experiment with neural networks on their
own data. The visualization possibilities of SynWorks make
access to the network easy and intuitive. The network's
behaviour can be watched and tracked, which in combination
with the included examples results in a good insight of
how the actual neural network is working.
On the other hand, people working on neural network projects
and application programmers will find SynWorks powerful
enough for most of their needs. Especially in the signal
processing field, neural networks are quite attractive.
With SynWorks, programmers can create a signal processing
or pattern recognition unit on the base of a neural network,
and use this network in their application by simple
WHAT ARE NEURAL NETWORKS ?
A neural network is a processing device, either an algorithm,
or actual hardware, whose design was motivated by the design
and functioning of the building blocks of the human brain,
such as neurons and connections (axons, dendrits).
It is realized as a network of many simple processing units
that are regularly interconnected. The connections carry
a numerical information, they are "weighted".
What makes neural networks very attractive is their ability
to "learn" from examples. Most neural networks have some
sort of "learning law" which describes how the weights of
connections are to be adjusted on the basis of presented
Probably the most popular neural networks are the feedforward
networks, with the backpropagation technique/generalized
delta rule being the learning law.
WHAT CAN NEURAL NETWORKS DO ?
In principle, NNs can compute any computable function,
i.e. they can do everything a normal digital computer
Especially can anything that can be represented as a
mapping between vector spaces be approximated to
arbitrary precision by feedforward NNs (which is the
most often used type).
In practice, NNs are especially useful for mapping
problems which are tolerant of a high error rate,
have lots of example data available, but to which
hard and fast rules can not easily be applied.
BOOKS ON NEURAL NETWORKS
(No completeness intended)
Hecht-Nielsen, R. (1990). Neurocomputing. Addison Wesley.
Aleksander, I. and Morton, H. (1990).
An Introduction to Neural Computing. Chapman and Hall
Beale, R. and Jackson, T. (1990). Neural Computing, an Introduction.
Adam Hilger, IOP Publishing Ltd.
Rumelhart, D. E. and McClelland, J. L. (1986).
Parallel Distributed Processing:
Explorations in the Microstructure of Cognition (volumes 1 & 2).
and lots more ...
FEATURES OF SYNWORKS
12 different standard network models
5 different possibilities to display network (simultaneously)
4 different possibilities to print network (standard WB printer,
colour printing supported)
24 different instruments to watch and track the network's
53 AREXX commands give full external control over SynWorks.
On-line context sensitive help system, supports keyword tracking.
Supports all resolutions higher than or equal to 640 x 400 with
at least two bitplanes (incl. A 2024, Productivity, AGA).
GUI according to User Interface Style Guide.
Version for 68020/68881 and up available.
C programming interface available.
Amiga 500, 1000, 1200, 2000, 2500, 3000, 4000, 1.5 MByte RAM,
Kickstart 2.04 and up.
Harddisk recommended, FPU (special program version) highly
recommended, Display enhancer/FF/AGA recommended.
ftp.wustl.edu (18.104.22.168) and its mirrors
Shareware fee: DM 130/US $80/Students
DM 200/US $120/Others
Demo version is freely distributable. Full version is
shareware, the ftp archive contains a demonstration version
with certain features disabled. Users of versions 1.0, 1.1
and 1.2 will be directly informed about upgrade possibilities,
all new users can get the full version as described above
directly from the author.
This version is now fully AGA compatible and contains some
The maintainers of Aminet.