On the Condition of Adaptive Neurofuzzy Models


Brown, M., An, P.E. and Harris, C.J. (1995) On the Condition of Adaptive Neurofuzzy Models. Int. Joint Conf. of the 4th Int. Conf. on Fuzzy Systems and the 2nd Int. Fuzzy Engineering Symp. , 663--670.

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Description/Abstract

Learning within fuzzy and neurofuzzy systems is becomingly increasingly important as researchers try to infer qualitative, vague information from quantitative, numeric data. The fuzzy representation of an adaptive neurofuzzy system is important both for initialisation and validation purposes, where a designer needs to interpret the knowledge stored in a network. Therefore it is important to study the convergence and rate of convergence characteristics of the parameters in a neurofuzzy model and investigate how this depends on the system's structure. This paper considers how the condition of the input fuzzy sets determines the convergence and generalisation abilities of the network and describes several new results about instantaneous least mean square training rules.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Organisation: IEEE/IFES Address: Yokohama, Japan
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 250243
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/250243

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