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山东大学学报(工学版) ›› 2010, Vol. 40 ›› Issue (3): 113-118.

• 土木工程 • 上一篇    下一篇

基于趋势检查法的遗传神经网络模型及工程应用

邱道宏1,张乐文1,崔伟2,苏茂鑫1,孙怀凤1   

  1. 1. 山东大学岩土与结构工程研究中心,  山东 济南 250061;
     2. 山东大学土建与水利学院, 山东 济南 250061
  • 收稿日期:2009-11-20 出版日期:2010-06-16 发布日期:2009-11-20
  • 作者简介:邱道宏(1980-),男,四川成都人,博士(博士后),讲师,主要研究方向为岩石力学、非线性方法. E-mail:qiudaohong@yahoo.com.cn
  • 基金资助:

    国家重点基础研究发展计划资助项目(2009CB724607);教育部科学技术研究重点资助项目(108158);国家自然科学基金资助项目(50908134);中国博士后科学基金资助项目(20090461203)

A genetic neural network model based on a trend examination method and engineering application

QIU Dao-hong1, ZHANG Le-wen1, CUI Wei2, SU Mao-xin1, SUN Huai-feng1   

  1. 1. Geotechnical and Structural Engineering Research Center, Shandong University, 250061, China;
    2. School of Civil Engineering, Shandong University, Jinan 250061, China
  • Received:2009-11-20 Online:2010-06-16 Published:2009-11-20

关键词: 神经网络, 可靠性, 样本, 权重, 围岩分类

Abstract:

Aiming at the model’s reliability problem of a neural network, a trend examination method was presented to check the model’s reliability. It checked the model through the influence trend of evaluation index to evaluation grade. The process of the method was  incessantly adjusting the model’s parameter, training, and trend examination, until  the best model was obtained. This method  presented  a new idea and can be used in any problems of model’s reliability examination based on the foreknowable experience method. To the problem of the contribution difference of samples, the method of weighted samples was used to preprocess the samples and the samples weight was used in the objective function of the neural network. Finally, a genetic algorithm was adopted to optimize the parameter of the  neural network and a GAANN model based on the trend examination method was established. The improved model was applied to practical engineering about surrounding rock classification and the results showed that this method can improve the neural network generalization ability and prediction accuracy.

Key words:  neural network, reliability, samples, weights, surrounding rock classification

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