Network of Excellence in Neural Networks


NEURONET - 8961

Work Area: Neural Networks, Adaptive Systems

Keywords neural networks


Start Date: to be announced / Status: starting

[ participants / contact ]


Goals, Activities and Structure

The industrial applicability of neural networks is being increasingly realised world-wide, covering applications in all the major industries under the headings of control, time-series analysis, pattern recognition and decision-making. Problems for which there are no specific rules, and which are hard for other techniques to solve, are particularly suited to the neural network approach. It is becoming increasingly interdisciplinary, with interests from engineering, computer science, physics, mathematics, chemistry, neurophysiology, psychology and others.

NEURONET aims to increase industrial awareness of neural network research activity; reduce duplication of research effort by better contact between groups; increase awareness of new theoretical results/hardware developments by application groups; increase applications of neural networks by industry to solve difficult problems; construct a general theoretical framework of neural networks to allow more effective construction of specialised networks to solve specific industrial problems, and enable faster implementation in software simulators and hardware devices of these more effective networks and deeper theoretical criteria.

The structure of NEURONET will consist of a Coordinating Node and 10 managing nodes. Each managing node will act as a centre for contacting and collaborating associate and industrial nodes, primarily in its local geographical area. NEURONET will be managed overall by an executive board, with committees handling human resources, research collaboration and planning, machine resources and industrial relations. Information will be disseminated through the network by e-mail, a newsletter and directories. Contacts will be made with ongoing EU neural network projects and with other related Networks of Excellence. Industrial penetration will be especially active, with collaborative links being made through industrial nodes. This will also be attempted through direct targeting of companies in specific industries as well as through the Newsletter, and through contacts in Government and large industrial companies in each of the European countries.

Further information about NEURONET is available from the NEURONET home page <URL:http://www.neuronet.ph.kcl.ac.uk/>.


Coordinator

King's College London - UK
Strand
UK- LONDON WC2R 2LS

Participants

Université Libre de Bruxelles - B
Katholieke Universiteit Leuven - B
Université Catholique de Louvain - B
EPFL - CH
Universität Bonn - D
Siemens AG - D
Universität JW Goethe - D
TU Denmark - DK
Universidad Autonoma de Madrid - E
Université - Paris 6 - F
ENS Lyon - F
ENS ULM Paris - F
INRIA - F
National Technical University of Athens - GR
University of Thessaloniki - GR
TEI Heraklion - GR
Università di Genova - I
Elsag-Bailey SpA - I
Università degli Studi di Milano - I
IIASS Salerno - I
Università di Roma "La Sapienza" - I
Universiteit van Nijmegen - NL
Philips - NL
Shell - NL
INESC - P
KTH - S
Chalmers University of Technology - S
Karolinska Institute - S
KTH - S
Helsinki University of Technology - SF
University of Helsinki - SF
Lappeenranta University of Technology - SF
Tampere University of Technology - SF
ERA Technology - UK
ICSTM - UK
Logica UK - UK
Queen's University Belfast - UK
Recognition Research - UK

CONTACT POINT

Prof. J.G. Taylor/Dr. M.D. Plumbley
tel +44/71 873 2214 / 44 71 873 2241
fax +44/71 873 2017
e-mail: UDAH057@bay.cc.kcl.ac.uk / mark@dcs.kcl.ac.uk


LTR synopses home page LTR work area index LTR acronym index LTR number index LTR Networks index
All synopses home page all acronyms index all numbers index

NEURONET - 8961, August 1994


please address enquiries to the ESPRIT Information Desk

html version of synopsis by Nick Cook