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.

Download

Full text not available from this repository.

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
Organisations: Southampton Wireless Group
ePrint ID: 250285
Date :
Date Event
1996Published
Date Deposited: 04 May 1999
Last Modified: 18 Apr 2017 00:23
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/250285

Actions (login required)

View Item View Item