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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2011

01.11.2011 | Original Article

Metaheuristics for the feedforward artificial neural network (ANN) architecture optimization problem

verfasst von: Adenilson R. Carvalho, Fernando M. Ramos, Antonio A. Chaves

Erschienen in: Neural Computing and Applications | Ausgabe 8/2011

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Abstract

This article deals with evolutionary artificial neural network (ANN) and aims to propose a systematic and automated way to find out a proper network architecture. To this, we adapt four metaheuristics to resolve the problem posed by the pursuit of optimum feedforward ANN architecture and introduced a new criteria to measure the ANN performance based on combination of training and generalization error. Also, it is proposed a new method for estimating the computational complexity of the ANN architecture based on the number of neurons and epochs needed to train the network. We implemented this approach in software and tested it for the problem of identification and estimation of pollution sources and for three separate benchmark data sets from UCI repository. The results show the proposed computational approach gives better performance than a human specialist, while offering many advantages over similar approaches found in the literature.
Vorheriger Artikel Integrating multicriteria PROMETHEE II method into a single-layer perceptron for two-class pattern classification
Nächster Artikel A new fuzzy neural network model for solving fuzzy linear programming problems and its applications

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Metadaten
Titel
Metaheuristics for the feedforward artificial neural network (ANN) architecture optimization problem
verfasst von
Adenilson R. Carvalho
Fernando M. Ramos
Antonio A. Chaves
Publikationsdatum
01.11.2011
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 8/2011
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-010-0504-3

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