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

Date

2007-12-01

Advisors

Journal Title

Journal ISSN

ISSN

0893-6080

Volume Title

Publisher

Neural Networks

Type

Article

Peer reviewed

Yes

Abstract

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.

Keywords

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.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)