Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada,
Cogn Neurodyn. 2009 Mar;3(1):47-81. doi: 10.1007/s11571-008-9036-2. Epub 2008 Feb 1.
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.
约束优化问题在各种科学和工程应用中都会出现。由于一些单个的递归神经网络在应用于实时工程应用的约束优化问题时表现出一些局限性，因此开发了合作递归神经网络方法来克服这些单个递归神经网络的缺点。本文详细调查了用于解决约束优化问题及其工程应用的合作递归神经网络的工作，并从最优解的收敛和模型复杂度的角度指出了它们的现有模型。我们提供了一些例子和比较，以显示这些模型在给定应用中的优势。
