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Towards Parsimonious High-Dimensional Neurofuzzy Systems

Record type: Conference or Workshop Item (Other)

A neurofuzzy system combines the positive attributes of a neural network and a fuzzy system by providing a transparent framework for representing linguistic rules with well defined modelling and learning characteristics. Unfortunately, their application is limited to problems involving a small number of input variables by the curse of dimensionality where the the size of the rule base and the training set increase as an exponential function of the input dimension. The curse can be alleviated by exploiting structure whereby the function to be approximated is additively decomposed into a series of smaller submodels each of which can be viewed as a conventional neurofuzzy system.

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Citation

Mills, D.J., Bossley, K.M., Brown, M. and Harris, C.J. (1995) Towards Parsimonious High-Dimensional Neurofuzzy Systems At World Congress on Neural Networks. , 717--720.

More information

Published date: 1995
Additional Information: Organisation: INNS Address: Washington, DC
Venue - Dates: World Congress on Neural Networks, 1995-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250269
URI: http://eprints.soton.ac.uk/id/eprint/250269
PURE UUID: 8badc817-3e3a-4d0b-a856-766e5ac8b8b4

Catalogue record

Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:43

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Contributors

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

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