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A parallel neurofuzzy learning and construction algorithm

A parallel neurofuzzy learning and construction algorithm
A parallel neurofuzzy learning and construction algorithm
This paper establishes a connection between a neurofuzzy network model with the Mixture of Experts Network (MEN) modelling approach. Based on this connection, a new neurofuzzy MEN construction algorithm is proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The new construction algorithm is based on a new parallel learning method in which each model rule is trained independently, in which the parameter convergence property of the new learning method is established. By using the expert selective criterion of the MEN model output sensitivity to each expert, each rule can be selected to be trained or inhibited. The construction method is effective in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector with the additional computational advantage of parallel processing. The proposed algorithm is analysed for effectiveness followed by a numerical example to illustrate the efficacy for some difficult data based modelling problem.
Harris, C. J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C. J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03

Harris, C. J. and Hong, X. (2001) A parallel neurofuzzy learning and construction algorithm.

Record type: Conference or Workshop Item (Other)

Abstract

This paper establishes a connection between a neurofuzzy network model with the Mixture of Experts Network (MEN) modelling approach. Based on this connection, a new neurofuzzy MEN construction algorithm is proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The new construction algorithm is based on a new parallel learning method in which each model rule is trained independently, in which the parameter convergence property of the new learning method is established. By using the expert selective criterion of the MEN model output sensitivity to each expert, each rule can be selected to be trained or inhibited. The construction method is effective in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector with the additional computational advantage of parallel processing. The proposed algorithm is analysed for effectiveness followed by a numerical example to illustrate the efficacy for some difficult data based modelling problem.

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More information

Published date: April 2001
Additional Information: Organisation: SPIE Aerosense 2001
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 252728
URI: http://eprints.soton.ac.uk/id/eprint/252728
PURE UUID: 0c5e21be-828b-48dd-a26d-acb44ff30f99

Catalogue record

Date deposited: 09 Jan 2001
Last modified: 16 Jul 2019 23:06

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Contributors

Author: C. J. Harris
Author: X. Hong

University divisions

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