Advances in Neurofuzzy Algorithms for Real-time Modelling and Control


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.

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Description/Abstract

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.

Item Type: Article
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 250285
Date Deposited: 04 May 1999
Last Modified: 27 Mar 2014 19:51
Further Information:Google Scholar
ISI Citation Count:12
URI: http://eprints.soton.ac.uk/id/eprint/250285

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