Pedro M. Talaván

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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(More)
The solution of an optimization problem through the continuous Hopfield network (CHN) is based on some energy or Lyapunov function, which decreases as the system evolves until a local minimum value is attained. A new energy function is proposed in this paper so that any 0-1 linear constrains programming with quadratic objective function can be solved. This(More)
For the Traveling Salesman Problem (£ ¥ ¤ § ¦) , a combinatorial optimization problem, a feed-forward artificial neural network model, the Continuous Hopfield Network (¨ ©) model, is used to solve it. This neural network approach is based on the solution of a differential equation. An appropriate parameter setting of this differential equation can assure(More)
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