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Synworks 2.1 available for FTP


        SynWorks - A design, training, test, and visualization environment for
                   neural networks.


        2.1, September 1993


        Michael Kaiser
        Rintheimer Strasse 59
        D 76131 Karlsruhe, FRG
        Phone : (+49) (721) 61 18 19
        EMail :


        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
        the network.
        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
        function calls.


        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.


        In principle, NNs can compute any computable function,
        i.e. they can do everything a normal digital computer
        can do.
        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.


        (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).
        MIT Press.

        and lots more ...


        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.


        Aminet: ( 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
        new demos.


        The maintainers of Aminet.