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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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
|Date Deposited:||04 May 1999|
|Last Modified:||27 Mar 2014 19:51|
|Further Information:||Google Scholar|
|ISI Citation Count:||0|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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