High Dimensional Neurofuzzy Systems: Overcoming the Curse of Dimensionality
High Dimensional Neurofuzzy Systems: Overcoming the Curse of Dimensionality
Many researchers do not appreciate the problems in building high-dimensional fuzzy models or control surfaces, yet this task has occupied researchers in several fields for the past thirty years. The problems occur due to the lack of both available training data and the required computational resources necessary for building and calculating the response of the model. This paper outlines several techniques for partially overcoming the curse of dimensionality associated with high-dimensional data modelling problems and compares and contrasts them with several algorithms developed in the statistical community. The work is intended to outline both conventional concepts which can be usefully applied in neurofuzzy models and new developments in this field.
2139--2146
Brown, M.
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Bossley, K.M.
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Mills, D.J.
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Harris, C.J.
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March 1995
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Bossley, K.M.
de1a2979-b9e9-481e-af09-0b4887f0f360
Mills, D.J.
bd207c8b-fbf0-41da-bba4-b54d9a29804d
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Brown, M., Bossley, K.M., Mills, D.J. and Harris, C.J.
(1995)
High Dimensional Neurofuzzy Systems: Overcoming the Curse of Dimensionality.
Proc. Int. Joint Conf. of the 4th Int. Conf. on Fuzzy Systems and the 2nd Int. Fuzzy Engineering Symp..
.
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Conference or Workshop Item
(Other)
Abstract
Many researchers do not appreciate the problems in building high-dimensional fuzzy models or control surfaces, yet this task has occupied researchers in several fields for the past thirty years. The problems occur due to the lack of both available training data and the required computational resources necessary for building and calculating the response of the model. This paper outlines several techniques for partially overcoming the curse of dimensionality associated with high-dimensional data modelling problems and compares and contrasts them with several algorithms developed in the statistical community. The work is intended to outline both conventional concepts which can be usefully applied in neurofuzzy models and new developments in this field.
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Published date: March 1995
Additional Information:
Organisation: IEEE/IFES Address: Yokohama, Japan
Venue - Dates:
Proc. Int. Joint Conf. of the 4th Int. Conf. on Fuzzy Systems and the 2nd Int. Fuzzy Engineering Symp., 1995-03-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250245
URI: http://eprints.soton.ac.uk/id/eprint/250245
PURE UUID: f349e679-97d9-4bb1-a959-7bfc74e40908
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Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07
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Contributors
Author:
M. Brown
Author:
K.M. Bossley
Author:
D.J. Mills
Author:
C.J. Harris
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