Analyzing the benefits of using a fuzzy-neuro model in the accuracy of the NeurAge system: An agent-based system for classification tasks

ABREU, M.C. da C. and CANUTO, A.M.P. (2006). Analyzing the benefits of using a fuzzy-neuro model in the accuracy of the NeurAge system: An agent-based system for classification tasks. IEEE International Conference on Neural Networks - Conference Proceedings, 2959-2966.

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Abstract

The use of intelligent agents in the structure of multi-classifier systems has been investigated in order to overcome some drawbacks of these systems and, as a consequence, to improve the performance of such systems. As a result of this, the NeurAge system was proposed. This system is composed by several neural agents which communicate (negotiate) a common result for the testing patterns. The NeurAge system has been successfully applied in some classification tasks. Basically, in these investigations, NeurAge has used multi-layer perceptrons (MLPs) as the neural network module of its agents. In this paper, it is presented an investigation of the use of the NeurAge System using other types of classifiers, mainly Fuzzy MLP. The main aim of this investigation is to analyze the benefits of using fuzzy neural networks in the performance of the NeurAge System. © 2006 IEEE.

Item Type:	Article
Identification Number:	https://doi.org/10.1109/ijcnn.2006.247251
Page Range:	2959-2966
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	06 Aug 2020 11:00
Last Modified:	17 Mar 2021 23:48
URI:	https://shura.shu.ac.uk/id/eprint/25388

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