计算机工程与应用 ›› 2007, Vol. 43 ›› Issue (18): 69-71.

• 学术探讨 • 上一篇    下一篇

基于最具影响粒子群优化的BP神经网络训练

王 慧,刘希玉   

  1. 山东师范大学 信息科学与工程学院,济南 250014
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-06-21 发布日期:2007-06-21
  • 通讯作者: 王 慧

Back-propagation neural network training based on particle swarm optimization with best influential partical

WANG Hui,LIU Xi-yu   

  1. School of Information Science and Engineering,Shandong Normal University,Ji’nan 250014,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-06-21 Published:2007-06-21
  • Contact: WANG Hui

摘要: 系统地介绍了粒子群优化算法,将粒子群优化算法用于BP神经网络的学习训练,提出了一种改进的粒子群算法——最具影响粒子PSO算法BIPSO,并利用复合适应度即均方误差和误差均匀度之和作为BIPSO训练神经网络的指标,并对它与其他的神经网络训练算法诸如BP算法、GA算法、PSO算法进行了比较。实验结果表明:BIPSO性能优于其他算法,更容易找到全局最优解,具有更好的收敛性。

Abstract: This paper systematically introduces particle swarm optimization algorithm and applies it to the training of neural networks.It advances an improved particle swarm optimization algorithm-Best Influential PSO(BIPSO),which regards multiple fitness degree(SE and EU) as the guildline of BIPSO and compares it with BP,GA and PSO algorithms.The result of experiment indicates that BIPSO has better performance than other algorithms,finds global best solution more easily and has better astringency.