Linear programming and network flows (2nd ed.)
Linear programming and network flows (2nd ed.)
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
A deterministic annealing neural network for convex programming
Neural Networks
Primal and dual assignment networks
IEEE Transactions on Neural Networks
“Optimal” Hopfield network for combinatorial optimization with linear cost function
IEEE Transactions on Neural Networks
Analysis and design of an analog sorting network
IEEE Transactions on Neural Networks
A new approach to solve the traveling salesman problem
Neurocomputing
A new neural network approach to the traveling salesman problem
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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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.