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

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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.