From: marwan@ee.su.oz.au (Marwan A. Jabri, Sydney Univ. Elec. Eng., Tel: +61-2 692 2240)
Subject: Multi-Module Neural Networks Environment
Reply-To: marwan@ee.su.oz.au (Marwan A. Jabri, Sydney Univ. Elec. Eng., Tel: +61-2 692 2240)
Organization: Electrical Engineering, The University of Sydney, Australia
Date: Mon, 5 Oct 1992 13:34:18 GMT

		Multi-Module Neural Computing Environment
				(MUME)

MUME 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.

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.

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.

The object oriented structure makes simple the addition of new
network classes and new learning algorithms. New classes/algorithms can be
simply added to the library or compiled into a program at run-time. The
interface between classes is performed using Network Service Functions
which can be easily created for a new class/algorithm.

The architectures and learning algorithms currently available are:


	Class				Learning algorithms
	------------			-------------------

	MLP				backprop, weight perturbation,
					node perturbation, summed weight
					perturbation

	SRN				backprop through time, weight
					update driven node splitting,
					History bound nets 

	CRRN				Williams and Zipser

	Programmable
	Limited precision nets		Weight perturbation, Combined
					Search Algorithm, Simulated Annealing
		
	
Other general purpose classes include (viewed as nets):

	o	DC source
	o	Time delays
	o	Random source
	o	FIFOs and LIFOs
	o	Winner-take-all
	o	X out of Y classifiers

The modules are provided in a library. Several "front-ends" or clients are
also available.

MUME can be used to include non-neural computing modules (decision 
trees, ...) in applications.

The software is the product of a number of staff and postgraduate students
at the Machine Intelligence Group at Sydney University Electrical
Engineering. It is currently being used in research, research and
development and teaching, in ECG and ICEG classification, and speech and
image recognition. As such, we are interested in institutions that
can exploit the tool (especially in educational courses) and build up on it.

The software is written in 'C' and is being used on Sun and DEC 
workstations. Efforts are underway to port it to the Fujitsu VP2200 
vector processor using the VCC vectorising C compiler.

MUME is made available to research institutions on media/doc/postage cost
arrangements. Information on how to acquire it may be obtained by writing
(or email) to:

Marwan Jabri
SEDAL
Sydney University Electrical Engineering
NSW 2006 Australia
Tel: (+61-2) 	692-2240
Fax: 		660-1228
Email:		marwan@sedal.su.oz.au


-------------------------------------------------------------------
Marwan Jabri				  Email: marwan@ee.su.oz.au
SEDAL, Electrical Engineering,		      Tel: (+61-2) 692-2240
Sydney University, NSW 2006, Australia 	      Fax: (+61-2) 692-3847
Newsgroups: comp.ai.neural-nets