Name
Abstract | ||
|---|---|---|
An efficient algorithm for the minimum vertex cover problem based on Hopfield neural network leaning is presented. The learning algorithm has two phases, the Hopfield network phase and the learning phase. When network gets stuck in local minimum, the learning phase is performed in an attempt to fill up the local minimum valley by modifying parameter in a gradient ascent direction of the energy function. The proposed algorithm is tested on benchmark graphs. The simulation results show that the proposed algorithm is an effective algorithm for the minimum vertex cover problem in terms of the computation time and solution quality. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28647-9_72 | ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1 |
Keywords | Field | DocType |
hopfield network,vertex cover | Graph,Gradient descent,Mathematical optimization,Computer science,Wake-sleep algorithm,Algorithm,Artificial intelligence,Vertex cover,Artificial neural network,Hopfield network,Machine learning,Computation | Conference |
Volume | ISSN | Citations |
3173 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoming Chen | 1 | 6 | 1.83 |
| Zheng Tang | 2 | 183 | 24.78 |
| Xinshun Xu | 3 | 390 | 32.51 |
| Li Songsong | 4 | 24 | 3.76 |
| Guangpu Xia | 5 | 6 | 2.27 |
| Jiahai Wang | 6 | 604 | 49.01 |