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: Proceedings of 2005 International Conference on Neural Networks and Brain : Oct. 13-15, 2005, Beijing, China / Mingsheng Zhao, Zhongzhi Shi. - Piscataway, NJ : IEEE Computer Society, 2005 Oct. - ISBN 0780394224. - pp. 115-120 (( convegno International Conference on Neural Networks and Brain, 2005. ICNN&B '05. tenutosi a Beijin, China nel 2005.
A Stochastic Neural Model for Graph Problems: Software and Hardware Implementation
G. GrossiPrimo
;F. PedersiniUltimo
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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