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

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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.

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  1. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada

    Mohamed S. Kamel

  2. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Youshen Xia

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  1. Mohamed S. Kamel
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Kamel, M.S., Xia, Y. Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications. Cogn Neurodyn 3, 47–81 (2009). https://doi.org/10.1007/s11571-008-9036-2

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