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.
Download
Full text not available from this repository.
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 and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control |
| Item ID: | 250116 |
| Date Deposited: | 04 May 1999 |
| Last Modified: | 02 Mar 2012 13:39 |
| Contributors: | Bossley, K.M. (Author) Brown, M. (Author) Harris, C.J. (Author) |
| Date: | 1996 |
| Additional Information: | Address: Aachen |
| Status: | Published |
| Further Information: | Google Scholar |
| URI: | http://eprints.soton.ac.uk/id/eprint/250116 |
Actions (login required)
 |
View Item |
