This article describes a novel neural stochastic model for solving graph problems. The neural system has been tested on random graphs, showing better performance than other well-known heuristics for the same problems. Furthermore, a simplified version of the proposed model has been developed in such a way that it can be easily implemented in hardware using programmable logic chips, such as FPGAs.
A stochastic neural model for graph problems: Software and hardware implementation / G. Grossi, F. Pedersini - In: 2005 International Conference on Neural Networks and Brain[s.l] : IEEE, 2005. - ISBN 0-7803-9422-4. - pp. 115-120 (( convegno International Conference on Neural Networks and Brain tenutosi a Beijing nel 2005 [10.1109/ICNNB.2005.1614579].
A stochastic neural model for graph problems: Software and hardware implementation
G. Grossi;F. Pedersini
2005
Abstract
This article describes a novel neural stochastic model for solving graph problems. The neural system has been tested on random graphs, showing better performance than other well-known heuristics for the same problems. Furthermore, a simplified version of the proposed model has been developed in such a way that it can be easily implemented in hardware using programmable logic chips, such as FPGAs.
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