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On the Condition of Adaptive Neurofuzzy Models

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

Record type: Conference or Workshop Item (Other)


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.

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Published date: 1995
Additional Information: Organisation: IEEE/IFES Address: Yokohama, Japan
Venue - Dates: Int. Joint Conf. of the 4th Int. Conf. on Fuzzy Systems and the 2nd Int. Fuzzy Engineering Symp., 1995-01-01
Organisations: Southampton Wireless Group


Local EPrints ID: 250243
PURE UUID: 48036180-91c6-454a-936c-489652ff3d3c

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Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:43

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Author: M. Brown
Author: P.E. An
Author: C.J. Harris

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