Bayesian Regularisation Applied to Neurofuzzy models


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

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Description/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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Address: Aachen
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 250116
Date Deposited: 04 May 1999
Last Modified: 27 Mar 2014 19:51
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/250116

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