On the Condition of Adaptive Neurofuzzy Models
On the Condition of Adaptive Neurofuzzy Models
Learning within fuzzy and neurofuzzy systems is becomingly increasingly important as researchers try to infer qualitative, vague information from quantitative, numeric data. The fuzzy representation of an adaptive neurofuzzy system is important both for initialisation and validation purposes, where a designer needs to interpret the knowledge stored in a network. Therefore it is important to study the convergence and rate of convergence characteristics of the parameters in a neurofuzzy model and investigate how this depends on the system's structure. This paper considers how the condition of the input fuzzy sets determines the convergence and generalisation abilities of the network and describes several new results about instantaneous least mean square training rules.
663--670
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
An, P.E.
5dc94657-d009-4d13-9a0f-6645a9d296d9
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
1995
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
An, P.E.
5dc94657-d009-4d13-9a0f-6645a9d296d9
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Brown, M., An, P.E. and Harris, C.J.
(1995)
On the Condition of Adaptive Neurofuzzy Models.
Int. Joint Conf. of the 4th Int. Conf. on Fuzzy Systems and the 2nd Int. Fuzzy Engineering Symp..
.
Record type:
Conference or Workshop Item
(Other)
Abstract
Learning within fuzzy and neurofuzzy systems is becomingly increasingly important as researchers try to infer qualitative, vague information from quantitative, numeric data. The fuzzy representation of an adaptive neurofuzzy system is important both for initialisation and validation purposes, where a designer needs to interpret the knowledge stored in a network. Therefore it is important to study the convergence and rate of convergence characteristics of the parameters in a neurofuzzy model and investigate how this depends on the system's structure. This paper considers how the condition of the input fuzzy sets determines the convergence and generalisation abilities of the network and describes several new results about instantaneous least mean square training rules.
This record has no associated files available for download.
More information
Published date: 1995
Additional Information:
Organisation: IEEE/IFES Address: Yokohama, Japan
Venue - Dates:
Int. Joint Conf. of the 4th Int. Conf. on Fuzzy Systems and the 2nd Int. Fuzzy Engineering Symp., 1995-01-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250243
URI: http://eprints.soton.ac.uk/id/eprint/250243
PURE UUID: 48036180-91c6-454a-936c-489652ff3d3c
Catalogue record
Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07
Export record
Contributors
Author:
M. Brown
Author:
P.E. An
Author:
C.J. Harris
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics