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


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

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Description/Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Organisation: IEEE Address: San Antonio, Texas
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 250265
Date Deposited: 04 May 1999
Last Modified: 27 Mar 2014 19:51
Further Information:Google Scholar
ISI Citation Count:0
URI: http://eprints.soton.ac.uk/id/eprint/250265

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