Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-03

AUTHORS

Mohamed S. Kamel, Youshen Xia

ABSTRACT

Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications. More... »

PAGES

47-81

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11571-008-9036-2

    DOI

    http://dx.doi.org/10.1007/s11571-008-9036-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1030880250

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/19003467


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