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Neurofuzzy Algorithms for Model Identification: Structure and Parameter Determination

Record type: Conference or Workshop Item (Other)

This paper describes some of the issues associated with developing a class of neurofuzzy construction algorithms based on B-spline fuzzy membership functions. These techniques have many desirable properties and links can be made with more conventional statistical model building approaches. The neurofuzzy model is decomposed into its linear and nonlinear components and a search technique is used to identify the structural nonlinearities whereas standard linear optimisation algorithms are used to identify the linear parameters. This paper discusses the performance of these two elements and contrasts their roles in the context of neurofuzzy systems.

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Citation

Brown, M., Bossley, K.M. and Harris, C.J. (1996) Neurofuzzy Algorithms for Model Identification: Structure and Parameter Determination At Computational Engineering in Systems Applications '96: Symposium on Control, Optimization and Supervision. , 1061--1066.

More information

Published date: 1996
Additional Information: Organisation: IMACS Address: Lille, France
Venue - Dates: Computational Engineering in Systems Applications '96: Symposium on Control, Optimization and Supervision, 1996-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250114
URI: http://eprints.soton.ac.uk/id/eprint/250114
PURE UUID: 92718d2f-d2ee-45ec-9b16-a186732a6f50

Catalogue record

Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:44

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Contributors

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

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