Name
Abstract | ||
|---|---|---|
The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance. © 2013 ACADEMY PUBLISHER. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.4304/jnw.8.11.2685-2691 | JNW |
Keywords | Field | DocType |
artificial bee colony,bp neural network,network traffic prediction,particle swarm optimization | Particle swarm optimization,Convergence (routing),Artificial bee colony algorithm,Computer science,Swarm intelligence,Evolutionary computation,Multi-swarm optimization,Artificial intelligence,Artificial neural network,Metaheuristic | Journal |
Volume | Issue | Citations |
8 | 11 | 2 |
PageRank | References | Authors |
0.40 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Zhu | 1 | 2 | 0.40 |
| Guanghua Zhang | 2 | 5 | 1.05 |
| Jing Qiu | 3 | 60 | 14.01 |