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Neurofuzzy mixture of experts network parallel learning and model construction algorithms

Harris, C. J. and Hong, X. (2001) Neurofuzzy mixture of experts network parallel learning and model construction algorithms. IEE Proceedings-Control Theory and Applications, 148 (6). pp. 456-465. ISSN 1350-2379

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To link to this article DOI: 10.1049/ip-cta:20010758

Abstract/Summary

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:18500
Uncontrolled Keywords:dimensionality , fuzzy neural network , least means squares , mixture of experts network , parallel algorithm , parametric learning , probability density , regression vector
Publisher:IET

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