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Advances in Neurofuzzy Algorithms for Real-time Modelling and Control

Record type: Article

This paper reviews the architecture, representation capability, training and learning ability of a class of adaptive neurofuzzy systems for real time modelling and control of unknown nonlinear dynamical processes. Issues relating to learning stability, training laws and parametric convergence, network conditioning, gradient noise, the curse of dimensionality associated with associative memory networks, automatic network construction algorithms, and a series of neurofuzzy control design laws, are discussed together with future critical research issues associated with neurofuzzy systems.

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Citation

Harris, C.J., Brown, M., Bossley, K.M., Mills, D.J. and Feng, M. (1996) Advances in Neurofuzzy Algorithms for Real-time Modelling and Control J. Engineering Application of AI, 9, (1), 1--16.

More information

Published date: 1996
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250285
URI: http://eprints.soton.ac.uk/id/eprint/250285
PURE UUID: 4a6b0029-4b33-4834-ae9e-8d0194f2984e

Catalogue record

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

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Contributors

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

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