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An Analysis of the Application of B-spline Neurofuzzy Construction Algorithms

Record type: Conference or Workshop Item (Other)

This paper investigates the application of the B-spline neurofuzzy construction algorithms, developed by the ISIS research group, to identifying appropriate models from two standard data sets. The two data sets are the Box-Jenkins data and a simulated system data set, both of which can are described by discrete time series models. It is shown that the construction algorithms pick out the dominant linear components associated with this data, as well as the small nonlinearities, and the model's structure facilitates the analysis of the data where errors occur. This is simplified by the incorporation of these techniques inside NeuFrame: a PC-based graphical development environment.

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Citation

Brown, M., Bossley, K.M. and Harris, C.J. (1996) An Analysis of the Application of B-spline Neurofuzzy Construction Algorithms At EUFIT '96. , 1830--1834.

More information

Published date: 1996
Venue - Dates: EUFIT '96, 1996-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250120
URI: http://eprints.soton.ac.uk/id/eprint/250120
PURE UUID: 80ddc286-71ee-49ae-9e19-9fdb689db5d2

Catalogue record

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

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Contributors

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

University divisions


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