东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2009, Vol. 30 ›› Issue (9): 1314-1317+1345.DOI: -

• 论著 • 上一篇    下一篇

一种RBF神经网络高精度算法研究及应用

郑夕健;张国忠;谢正义;   

  1. 东北大学机械工程与自动化学院;沈阳建筑大学交通与机械工程学院;
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2009-09-15 发布日期:2013-06-22
  • 通讯作者: Zheng, X.-J.
  • 作者简介:-
  • 基金资助:
    国家“十一五”科技支撑计划重点项目(2006BAJ12B05);;

Research on a high-precision algorithm of RBF neural network and its applications

Zheng, Xi-Jian (1); Zhang, Guo-Zhong (1); Xie, Zheng-Yi (2)   

  1. (1) School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China; (2) School of Traffic and Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2009-09-15 Published:2013-06-22
  • Contact: Zheng, X.-J.
  • About author:-
  • Supported by:
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摘要： 剖析了RBF神经网络基本算法的原理以及激励函数参量与隐层单元数量按经验选取所带来的问题.基于RBF神经网络结构,以网络的权阈值为设计变量,网络误差为目标函数,通过合理的动态变量排序,构建了一种RBF神经网络的新的高精度算法,并编制计算程序.与RBF网络基本算法相比,这种算法是以权阈值为未知变量的真实优化过程,实现了RBF神经网络的高精度计算.从方程论理论出发,给出了网络隐层结构的合理确定方法.通过实例的程序分析,表明了该优化算法具有较高的样本拟合与插值精度,为进一步理论研究与工程应用提供基础.

关键词: 神经网络, 径向基函数(RBF), 高精度算法, 网络参数, 优化

Abstract: Analyzes the principle of the basic algorithm of RBF neural network and the problem that the excitation function parameters and the number of hidden layer elements are both selected empirically. A new high-precision algorithm of RBF neural network is proposed according to RBF neural network structure where the threshold and weight values are taken as design variables with network error as objective function, and all of the dynamic variables are ranked reasonably with a computation program given. Compared with the basic algorithm of RBF neural network, the algorithm proposed is really an optimization process taking the threshold and weight values as unknown variables so as to implement the high-precision computation of the RBF neural network. According to the equation theory, a reasonable way is given to determine the hidden layer structure in network. The program analysis by examples indicated that the optimization algorithm has a high goodness of fit with samples and high-precision interpolation results, thus providing the foundation for further theoretic study and engineering applications.

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