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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2009

01.11.2009 | Original Article

A variant of the SOM algorithm and its interpretation in the viewpoint of social influence and learning

verfasst von: Mu-Chun Su, Yu-Xiang Zhao

Erschienen in: Neural Computing and Applications | Ausgabe 8/2009

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Abstract

The conventional self-organizing feature map (SOM) algorithm is usually interpreted as a computational model, which can capture main features of computational maps in the brain. In this paper, we present a variant of the SOM algorithm called the SOM-based optimization (SOMO) algorithm. The development of the SOMO algorithm was motivated by exploring the possibility of applying the SOM algorithm in continuous optimization problems. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited by the SOMO algorithm. In our opinion, the SOMO algorithm not only can be regarded as a biologically inspired computational model but also may be regarded as a new approach to a model of social influence and social learning. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm.
Vorheriger Artikel Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming
Nächster Artikel A hierarchical hybrid neural model with time integrators in long-term load forecasting

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Metadaten
Titel
A variant of the SOM algorithm and its interpretation in the viewpoint of social influence and learning
verfasst von
Mu-Chun Su
Yu-Xiang Zhao
Publikationsdatum
01.11.2009
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 8/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-009-0278-7

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