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基于人工神经网络的三峡水库库岸稳定性分级

徐佩华1,2,陈剑平1,阙金声1,仲志成3,王 清1   

  1. 1.吉林大学 建设工程学院,长春 130026;2.成都理工大学 地质灾害防治与地质环境保护国家专业实验室,成都 610059;3.吉林大学 计算机学院,长春 130012
  • 收稿日期:2006-06-17 修回日期:1900-01-01 出版日期:2007-05-26 发布日期:2007-05-26
  • 通讯作者: 徐佩华

The Grading Model of Reservoir Bank Stability of Three Gorges Based on Artificial Neural Network Method

XU Pei-hua1,2,CHEN Jian-ping1,QUE Jin-sheng1, ZHONG Zhi-cheng3, WANG Qing1   

  1. 1.College of Construction Engineering, Jilin University, Changchun 130026, China;2.National Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu 610059,China;3.College of Computer, Jilin University, Changchun 130012, China
  • Received:2006-06-17 Revised:1900-01-01 Online:2007-05-26 Published:2007-05-26
  • Contact: XU Pei-hua

摘要: 为避免库岸稳定性评价法的随意性和不确定性,尝试采用具有处理非线性关系功能的人工神经网络方法进行库岸稳定性分级,为此构建了15-31-4结构的三层BP网络。该网络采用BP弹性算法,同时对初始权值和阀值进行了优化,实现了网络的非线性映射,并有着极快的收敛速度。用该BP网络对三峡水库的上游段库岸进行了稳定性等级判断,其结果与常规计算方法所得的结果基本相似。

关键词: 人工神经网络, BP弹性算法, 库岸稳定性

Abstract: In order to avoid the random and uncertainty of the assessment method of reservoir bank stability , the artificial neural network(ANN) method with the function of disposing the nonlinear relation was applied to judge the grade of stability. A three-layer BP network model was established with 15 input nodes, 31 nodes in hided layer and 4 output nodes . In this network model, the BP elastic algorithm(RPROP)was adopted and the initialized weight value and valve were optimized and this model has realized the nonlinear reflection of the network and has the fast speed of convergence. This established method of the three layers of BP network is an effective method worthy of popularizing. Case study in the upstream banks of reservoir of Three Gorges shows that the judgment of stability grade of reservoir bank with this BP network mentioned above was similar to the results of conventional calculation methods.

Key words: artificial neural network, the BP elastic algorithm, the stability of reservoir bank

中图分类号: 

  • TV62
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