DocumentCode
1563098
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
Volume
1
fYear
2005
Firstpage
115
Lastpage
120
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-9422-4
Type
conf
DOI
10.1109/ICNNB.2005.1614579
Filename
1614579
Link To Document