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|Title:||L-p Fuzzy ARTMAP neural network architecture||Authors:||Dagher, Issam||Affiliations:||Department of Computer Engineering||Keywords:||L-p norm
|Issue Date:||2006||Part of:||Journal of soft computing||Volume:||10||Issue:||8||Start page:||649||End page:||656||Abstract:||
In this paper, an L-p based Fuzzy ARTMAP neural network is presented. The category choice of this network is based on the L-p norm. Geometrical properties of this architecture are presented. Comparisons between this category choice and the category choice of the Fuzzy ARTMAP are illustrated. And simulation results on the databases taken from the UCI repository are performed. It will be shown that using the L-p norm is geometrically more attractive. It will operate directly on the input patterns without the need for doing any preprocessing. It should be noted that the Fuzzy ARTMAP architecture requires two preprocessing steps: normalization and complement coding. Simulation results on different databases show the good generalization performance of the L-p Fuzzy ARTMAP compared to the performance of Fuzzy ARTMAP.
This paper was presented in " 8th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2004) .July 18-21, 2004. Orlando, USA. ".
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/2183||Ezproxy URL:||Link to full text||Type:||Journal Article|
|Appears in Collections:||Department of Computer Engineering|
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