The University of Southampton
University of Southampton Institutional Repository

Construction and Design of Parsimonious Neurofuzzy Systems

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.
de1a2979-b9e9-481e-af09-0b4887f0f360
Mills, D.J.
bd207c8b-fbf0-41da-bba4-b54d9a29804d
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hunt, K.J.
532666af-4460-4554-9088-46a7805d84ba
Irwin, G.R.
3a2e6d1b-186e-4f86-8015-c5d7ccbf17d6
Warwick, K.
61ec73ef-55ab-434c-9f6d-56f283b44f09
Bossley, K.M.
de1a2979-b9e9-481e-af09-0b4887f0f360
Mills, D.J.
bd207c8b-fbf0-41da-bba4-b54d9a29804d
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hunt, K.J.
532666af-4460-4554-9088-46a7805d84ba
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. 153--177 .

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.

Full text not available from this repository.

More information

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

Catalogue record

Date deposited: 04 May 1999
Last modified: 29 Jan 2020 14:56

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×