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SNNS: Stuttgart Neural Network Simulator

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 SNNS simulator contains a simulator kernel written in ANSI C and a 2D/3D graphical user interface running under X11R4/X11R5. SNNS includes the following learning procedures: backpropagation (online, batch, with momentum and flat spot elimin., time delay), counterpropagation, quickprop, backpercolation 1, and generalized radial basis functions (RBF), RProp, recurrent ART-1, ART-2 and ARTMAP, Cascade Correlation and Recurrent Cascade Correlation, Dynamic LVQ, and Time delay networks (TDNN). (Elman networks and some other network paradigms have already been implemented but are scheduled for a later release.) NESSUS is a language for the description of neural networks. The Nessus compiler creates a network description that can be read by SNNS. SNNS2C is a tool to convert the description of a neural network from SNNS format to C source code. This code can then be linked to an existing program as a subroutine. SNNS is among the most popular neural network simulators.
Origin: [].
   as the files SNNSv2.1.tar.Z,,
   SNNS2Cv2.1.tar.Z, and NESSUSv2.1.tar.Z

Version: SNNS 3.1; Nessus 2.1; SNNS2C 2.1 Requires: ANSI C, X11 Ports: It runs under Sun Sparc (SLC, ELC, SS2, GX, GS), DECstation (2100, 3100, 5000/200), IBM RS 6000, HP 9000, and IBM-PC (386/486). Copying: Copyright (c) 1990-93 University of Stuttgart, IPVR, FRG. Use, copying, and distribution permitted. Modification prohibited. (Modifications may be distributed as separate patch files.) CD-ROM: Prime Time Freeware for AI, Issue 1-1 Mailing List: To be added to the mailing list, send a message to with subscribe snns in the message body. Author(s): Andreas Zell Guenter Mamier University of Stuttgart, IPVR Breitwiesenstrasse 20-22, W-7000 Stuttgart 80, Germany Keywords: ART-1, ART-2, ARTMAP, Authors!Mamier, Authors!Zell, Backpercolation, Backpropagation, C!Code, Cascade Correlation, Counterpropagation, Dynamic LVQ, LVQ, Machine Learning!Neural Networks, NESSUS, NETtalk, Neural Networks!Description Languages, Neural Networks!Simulators, Quickprop, RBF, RProp, Radial Basis Functions, Recurrent ART-1, Recurrent Cascade Correlation, SNNS, Stuttgart Neural Network Simulator, TDNN, Time Delay Neural Networks References: ?
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