Neural computing: theory and practice
Neural computing: theory and practice
A hybrid neural approach to combinatorial optimization
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Modern Control System Theory
Approximating maximum clique with a Hopfield network
IEEE Transactions on Neural Networks
A continuous Hopfield network equilibrium points algorithm
Computers and Operations Research
The Hopfield-Tank neural network applied to the mobile agent planning problem
Applied Intelligence
Solving TSP by using Lotka-Volterra neural networks
Neurocomputing
Theoretical analysis and parameter setting of hopfield neural networks
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Four-Quadrant division with HNN for euclidean TSP
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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The major drawbacks of the continuous Hopfield network (CHN) model when it is used to solve some combinatorial problems, for instance, the traveling salesman problem (TSP), are the non feasibility of the obtained solutions and the trial-and-error setting values process of the model parameters. In this paper, both drawbacks are avoided by introducing a set of analytical conditions guaranteeing that any equilibrium point of the CHN characterizes a tour for the TSP. In this way, any instance of the TSP can be solved with this parameter setting. Some computational experiences are also included, allowing the solution of instances with sizes of up to 1000 cities.