SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
A new approach to solve the traveling salesman problem
Neurocomputing
A new one-layer neural network for linear and quadratic programming
IEEE Transactions on Neural Networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Solving the assignment problem with the improved dual neural network
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A delayed lagrangian network for solving quadratic programming problems with equality constraints
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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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This paper presents two recurrent neural networks for solving the assignment problem. Simplifying the architecture of a recurrent neural network based on the primal assignment problem, the first recurrent neural network, called the primal assignment network, has less complex connectivity than its predecessor. The second recurrent neural network, called the dual assignment network, based on the dual assignment problem, is even simpler in architecture than the primal assignment network. The primal and dual assignment networks are guaranteed to make optimal assignment. The applications of the primal and dual assignment networks for sorting and shortest-path routing are discussed. The performance and operating characteristics of the dual assignment network are demonstrated by means of illustrative examples