计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (35): 233-235.DOI: 10.3778/j.issn.1002-8331.2009.35.070

• 工程与应用 • 上一篇    下一篇

基于改进粒子群算法的BP神经网络及其应用

徐以山,曾 碧,尹秀文,卢博生   

  1. 广东工业大学 计算机学院,广州 510075
  • 收稿日期:2008-07-09 修回日期:2008-10-13 出版日期:2009-12-11 发布日期:2009-12-11
  • 通讯作者: 徐以山

BP Neural Network and its applications based on improved PSO

XU Yi-shan,ZENG Bi,YIN Xiu-wen,LU Bo-sheng   

  1. College of Computer,Guangdong University of Technology,Guangzhou 510075,China
  • Received:2008-07-09 Revised:2008-10-13 Online:2009-12-11 Published:2009-12-11
  • Contact: XU Yi-shan

摘要: 目前BP神经网络是一种有效的预测方法,但在实际应用当中存在着一些自身的缺点,为此提出了一种基于改进粒子群算法的BP神经网络。通过动态调整粒子群算法中的惯性因子ω,有效地增强了算法对非线性问题的处理能力,同时提高了算法的收敛速度和搜索全局最优值的能力。建立改进后的BP网络模型,通过该模型和逐步回归方法对某市降水量进行实例分析。分析结果表明,改进后的BP网络模型具有较高的准备预报能力和稳定性。

Abstract: BP Neural Network is an effective method for prediction so far,but there are some self-determinations in its practical applications,so a new BP Neural Network based on improved Particle Swarm Optimization(PSO) is proposed.The capacity of solving nonlinear problems of this algorithm is enhanced effectively through adjusting inertia factor ω dynamically.At the same time,the convergence speed of this algorithm and the capacity of searching global extremum is increased.The improved BP Network model is built up,and the rainfall in a certain city is analyzed by this model and gradually regression method.Evidence shows this model has high accuracy and stability.

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