Analysis and test of efficient methods for building recursive deterministic perceptron neural networks.

De Montfort University Open Research Archive

Analysis and test of efficient methods for building recursive deterministic perceptron neural networks.

Elizondo, David ; Birkenhead, Ralph, 1955- ; Gongora, Mario Augusto ; Luyima, P. ; Taillard, Eric
Subject: RAE 2008, UoA 23 Computer Science and Informatics, recursive deterministic perceptron, batch learning, incremental learning, modular learning, performance sensitivity analysis, convergence time, topology
Citation: Elizondo, D.A. et al. (2007) Analysis and test of efficient methods for building recursive deterministic perceptron neural networks. Neural Networks, 20 (10), pp. 1095-1108.

Description:

This paper introduces a comparison study of three existing methods for building Recursive Deterministic Perceptron Neural Networks. Three methods were compared in terms of their level of generalisation, convergence time and topology sizes. Prior to this study only an exhaustive, NP-Complete method was used for building RDP neural networks. Due to its high complexity, this limited its use in real world classification problems. This work shows that the other two methods, with a polynomial time complexity, can be used as an alternative. These results will widen the use of the RDP neural network. The impact factor is 2.000.
URI: http://hdl.handle.net/2086/175
DOI: http://dx.doi.org/10.1016/J.neunet.2007.07.009
ISBN: 0893-6080
Research Group: DIGITS
 Research Group: Centre for Computational Intelligence
Peer Reviewed: Yes
Date: 2007-12-01

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