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MUME: Multi-Module Neural Computing Environment

areas/neural/systems/mume/
MUME (Multi-Module Neural Computing Environment) is a simulation environment for multi-modules neural computing. It provides an object oriented facility for the simulation and training of multiple nets with various architectures and learning algorithms. The object oriented structure makes simple the addition of new network classes and new learning algorithms. MUME includes a library of network architectures including feedforward, simple recurrent, and continuously running recurrent neural networks. Each architecture is supported by a variety of learning algorithms, including backprop, weight perturbation, node perturbation, and simulated annealing. MUME can be used for large scale neural network simulations as it provides support for learning in multi-net environments. It also provide pre- and post-processing facilities. MUME can be used to include non-neural computing modules (decision trees, etc.) in applications. MUME is being developed at the Machine Intelligence Group at Sydney University Electrical Engineering. 
Origin:   

   mickey.sedal.su.oz.au:/pub/ [129.78.24.170]
   as the files license.ps, mume-overview.ps.Z
   brutus.ee.su.oz.au:/pub/MUME-0.5-DOS.zip
Version: 0.5 Requires: C. Ports: Sun and DEC workstations. Efforts are underway to port it to the Fujitsu VP2200 vector processor using the VCC vectorizing C compiler, HP 9000/700, SGI workstations, DEC Alphas, and PC DOS (with DJGCC). Copying: MUME is available to research institutions on a media/doc/postage cost arrangement after signing a license agreement. The license agreement is included in this directory, as are the DOS executables. CD-ROM: Prime Time Freeware for AI, Issue 1-1 Bug Reports: mume-bugs@sedal.su.oz.au Mailing List: To be added to the mailing list, send email to mume-request@sedal.su.oz.au. Author(s): Marwan Jabri SEDAL Sydney University Electrical Engineering NSW 2006 Australia Tel: +61-2-692-2240 Fax: +61-2-660-1228 Keywords: Authors!Jabri, Backpropagation, Decision Trees, Feedforward Neural Networks, MUME, Machine Learning!Neural Networks, Neural Networks!Simulators, Node Perturbation, Recurrent Neural Networks, Simulated Annealing, Weight Perturbation References: ?
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