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Neurofuzzy networks for online modelling and control with provable learning and stability conditions

Record type: Conference or Workshop Item (Other)

This paper considers a wide class of basis associative memory networks and their learning and network conditioning for online modelling and control. It is shown that the networks parameter convergence rate, stability and gradient noise all depend upon the condition number C(R) of the basis function autocorrelation function R. This analysis shows that for online modelling networks should be locally generalising and have condition number tending to unity.

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Citation

Harris, C.J. and Brown, M. (1994) Neurofuzzy networks for online modelling and control with provable learning and stability conditions At Int. Conf. on Systems, Man, and Cybernetics. , 1469--1474.

More information

Published date: 1994
Additional Information: Organisation: IEEE Address: San Antonio, Texas
Venue - Dates: Int. Conf. on Systems, Man, and Cybernetics, 1994-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250265
URI: http://eprints.soton.ac.uk/id/eprint/250265
PURE UUID: 9221b0bb-2b3c-4d89-adf9-03d3d64efbed

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

University divisions


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