Abstract
This paper presents a multivalued Hopfield-type neural network as a method for solving combinatorial optimization problems with a formulation free of fine-tuning parameters.
As benchmark of the performance of the network we have used N-Queen problems. Computer simulations confirm that this network obtains good results when is compared with other neural networks.
It is shown also that different dynamics are easily formulated for the network leading to obtain more sophisticated algorithms with better performance.
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© 2001 Springer-Verlag Berlin Heidelberg
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Mérida, E., Muñoz, J., Benétez, R. (2001). A Recurrent Multivalued Neural Network for the N-Queens Problem. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_62
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DOI: https://doi.org/10.1007/3-540-45720-8_62
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