Title :
A Stochastic Neural Model for Graph Problems: Software and Hardware Implementation
Author :
Grossi, Giuliano ; Pedersini, Federico
Author_Institution :
Dipartimento di Sci. dell´´Informazione, Universita degli Studi di Milano, Milan
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
Keywords :
Hopfield neural nets; field programmable gate arrays; graph theory; stochastic processes; FPGA; graph problems; programmable logic chips; random graphs; stochastic neural model; Constraint optimization; Cost function; Electronic mail; Field programmable gate arrays; Hardware; Integrated circuit synthesis; Network synthesis; Programmable logic arrays; Stochastic processes; System testing;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614579
