Construction and Design of Parsimonious Neurofuzzy Systems
Construction and Design of Parsimonious Neurofuzzy Systems
Static fuzzy systems have been extensively applied in the Far East to a wide range of consumer products whereas researchers in the west have mainly been concerned with developing adaptive neural network that can learn to perform ill-defined, difficult tasks. Neurofuzzy systems attempt to combine the best aspects of each of these techniques as the transparent representation of a fuzzy system is fused with the adaptive capabilities of a neural network, while minimising the undesirable features. As such, they are applicable to a wide range of static, design problems and on-line adaptive modelling and control applications. This chapter focuses on how an appropriate structure for the rule base may be determined directly from a set of training data. It provides the designer with valuable qualitative information about the physics of the underlying process as well as improving the network's generalisation abilities and the condition of the learning problem.
153--177
Bossley, K.M.
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Mills, D.J.
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Brown, M.
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Harris, C.J.
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Hunt, K.J.
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Irwin, G.R.
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Warwick, K.
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1995
Bossley, K.M.
de1a2979-b9e9-481e-af09-0b4887f0f360
Mills, D.J.
bd207c8b-fbf0-41da-bba4-b54d9a29804d
Brown, M.
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Harris, C.J.
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Hunt, K.J.
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Irwin, G.R.
3a2e6d1b-186e-4f86-8015-c5d7ccbf17d6
Warwick, K.
61ec73ef-55ab-434c-9f6d-56f283b44f09
Bossley, K.M., Mills, D.J., Brown, M. and Harris, C.J.
(1995)
Construction and Design of Parsimonious Neurofuzzy Systems.
Hunt, K.J., Irwin, G.R. and Warwick, K.
(eds.)
Neural Network Engineering in Control.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
Static fuzzy systems have been extensively applied in the Far East to a wide range of consumer products whereas researchers in the west have mainly been concerned with developing adaptive neural network that can learn to perform ill-defined, difficult tasks. Neurofuzzy systems attempt to combine the best aspects of each of these techniques as the transparent representation of a fuzzy system is fused with the adaptive capabilities of a neural network, while minimising the undesirable features. As such, they are applicable to a wide range of static, design problems and on-line adaptive modelling and control applications. This chapter focuses on how an appropriate structure for the rule base may be determined directly from a set of training data. It provides the designer with valuable qualitative information about the physics of the underlying process as well as improving the network's generalisation abilities and the condition of the learning problem.
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Published date: 1995
Additional Information:
Address: London
Venue - Dates:
Neural Network Engineering in Control, 1995-01-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250144
URI: http://eprints.soton.ac.uk/id/eprint/250144
PURE UUID: f12dc862-4682-496e-9299-555df2d1bf98
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Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07
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Contributors
Author:
K.M. Bossley
Author:
D.J. Mills
Author:
M. Brown
Author:
C.J. Harris
Editor:
K.J. Hunt
Editor:
G.R. Irwin
Editor:
K. Warwick
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