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Bayesian Regularisation Applied to Neurofuzzy models

Bossley, K.M., Brown, M. and Harris, C.J. (1996) Bayesian Regularisation Applied to Neurofuzzy models At EUFIT '96. , 757--761.

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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More information

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

Catalogue record

Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:44

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Contributors

Author: K.M. Bossley
Author: M. Brown
Author: C.J. Harris

University divisions

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