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Application of the 'Winner Takes All' Principle in Wang's Recurrent Neural Network for the Assignment Problem

机译:“获奖者的应用在王的经常性神经网络中取得了所有”的分配问题

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摘要

One technique that uses Wang's Recurrent Neural Networks with the "Winner Takes All" principle is presented to solve the Assignment problem. With proper choices for the parameters of the Recurrent Neural Network, this technique reveals to be efficient solving the Assignment problem in real time. In cases of multiple optimal solutions or very closer optimal solutions, the Wang's Neural Network does not converge. The proposed technique solves these types of problem. Comparisons between some traditional ways to adjust the RNN' s parameters are made, and some proposals concerning to parameters with dispersion measures of the problem's cost matrix' coefficients are show.
机译:使用王的经常性神经网络与“获胜者采取所有”原则的一种技术,以解决分配问题。对于经常性神经网络的参数进行适当的选择,该技术揭示了实时有效解决了分配问题。在多种最佳解决方案或非常接近最佳解决方案的情况下,王的神经网络不会收敛。所提出的技术解决了这些类型的问题。制作了一些传统方式之间的传统方式的比较,并且有一些关于问题成本矩阵系数的分散测量的参数的提案。

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