Adaptive Neurofuzzy Systems for Difficult Modelling and Control Problems
Adaptive Neurofuzzy Systems for Difficult Modelling and Control Problems
This paper briefly describes how neurofuzzy systems combine the linguistic representation of fuzzy logic with the learning abilities and structure of a neural network. Several training algorithms are briefly described and several inductive learning procedures are outlined.
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
1994
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Brown, M. and Harris, C.J.
(1994)
Adaptive Neurofuzzy Systems for Difficult Modelling and Control Problems.
Colloq on Advances in Neural Networks for Control and Systems.
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Abstract
This paper briefly describes how neurofuzzy systems combine the linguistic representation of fuzzy logic with the learning abilities and structure of a neural network. Several training algorithms are briefly described and several inductive learning procedures are outlined.
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Published date: 1994
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Organisation: IEE Address: Berlin, Germany
Venue - Dates:
Colloq on Advances in Neural Networks for Control and Systems, 1994-01-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250252
URI: http://eprints.soton.ac.uk/id/eprint/250252
PURE UUID: 48237a93-ebc3-48ee-8461-0969b5df26f1
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Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07
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Contributors
Author:
M. Brown
Author:
C.J. Harris
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