互联网 qkzz.net
全刊杂志网:首页 > 女性 > 文章正文
刊社推荐

基于径向基函数神经网络的泥石流危险性评价


□ 陈 刚 何政伟 杨 洋 杨 斌

   (1.成都理工大学 a.信息工程学院;b.地球科学学院, 成都 610059;2.中石化西南油气分公司 勘探开发研究院 信息中心, 成都 610081)
  摘 要:泥石流危险性的主要评价指标与危险程度之间有着某种复杂的非线性的关系,通常采用统计分析、模糊评价、BP神经网络等评价方法,但这些方法均存在不足之处,难以进行准确评价。为了克服以上方法的不足,结合泥石流危险性评价指标,建立了基于径向基函数神经网络的泥石流危险性评价模型,并将该模型结果与BP神经网络的评价结果进行了对比。实验结果表明,径向基函数神经网络的模拟结果比BP神经网络更接近测量数据,精度更高,训练所需时间更少。因此,径向基函数神经网络经过充分训练后,能够较为准确地对泥石流的危险性进行评价,具有较好的应用价值。
  关键词:径向;网络;泥石流;评价
  中图分类号:TP301 文献标志码:A
   文章编号:10013695(2009)01024103
  
  Risk assessment of debris flow based on radial basis function neural network
  CHEN Gang1a,2,HE Zhengwei1b,YANG Yang1a,YANG Bin1a
  (1.a.College of Information Engineering, b.College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China;2.Information Center, Exploration & Production Research Institute, SWPB SINOPEC, Chengdu 610081, China)
  Abstract:There were some nonlinear relation between main assessment indexes of debris flow and risk. Statistics analysis, fuzzy evaluation,BP network and other methods were usually adopted. However, they had some insufficiencies and they were difficult to accurately assess risk. In order to overcome insufficiencies of these methods, this paper combined with assessment indexes of debris flow and construct risk assessment model of debris flow based on radial basis function neural network (RBFNN).It also conducts a contrast of assesment result between the RBFNN model and BPNN.Experiment shows that, compared with BPNN, RBFNN simulation have higher data precision, less training time and closer to measurement data. Therefore, RBFNN is capable of making more precise risk assessment of debris flow after enough training, and it’s more valuable in application. ......
很抱歉,暂无全文,若需要阅读全文或喜欢本刊物请联系《计算机应用研究》杂志社购买。
欢迎作者提供全文,请点击编辑
分享:
 

了解更多资讯,请关注“木兰百花园”
分享:
 
精彩图文


关键字
支持中国杂志产业发展,请购买、订阅纸质杂志,欢迎杂志社提供过刊、样刊及电子版。
关于我们 | 网站声明 | 刊社管理 | 网站地图 | 联系方式 | 中图分类法 | RSS 2.0订阅 | IP查询
全刊杂志赏析网 2017