Bayesian Regularisation Applied to Neurofuzzy models
Bayesian Regularisation Applied to Neurofuzzy models
Due to the inherent structure of neurofuzzy systems they are prone to poor generalisation. This paper discusses two complementary methods which should be employed to maximally exploit the available linguistic and numerical data, to overcome this problem. The main emphasis is the application of Bayesian regularisation to additive B-spline neurofuzzy models. This produces models which generalise well with poor quality data and hence identify more reliable rule bases. Error bars are then used to identify possible weakness in the resulting rule base, which require further validation.
757--761
Bossley, K.M.
de1a2979-b9e9-481e-af09-0b4887f0f360
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
1996
Bossley, K.M.
de1a2979-b9e9-481e-af09-0b4887f0f360
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Bossley, K.M., Brown, M. and Harris, C.J.
(1996)
Bayesian Regularisation Applied to Neurofuzzy models.
EUFIT '96.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
Due to the inherent structure of neurofuzzy systems they are prone to poor generalisation. This paper discusses two complementary methods which should be employed to maximally exploit the available linguistic and numerical data, to overcome this problem. The main emphasis is the application of Bayesian regularisation to additive B-spline neurofuzzy models. This produces models which generalise well with poor quality data and hence identify more reliable rule bases. Error bars are then used to identify possible weakness in the resulting rule base, which require further validation.
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Published date: 1996
Additional Information:
Address: Aachen
Venue - Dates:
EUFIT '96, 1996-01-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250116
URI: http://eprints.soton.ac.uk/id/eprint/250116
PURE UUID: 1d99bec5-c9f2-47a1-b511-ab2685372582
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Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:06
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Contributors
Author:
K.M. Bossley
Author:
M. Brown
Author:
C.J. Harris
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