| 神经网络模型在坝基扬压力影响因子量化分析中的应用 |
| |
| 引用本文： | 周剑,宋汉周.神经网络模型在坝基扬压力影响因子量化分析中的应用[J].水文地质工程地质,2005,32(3):38-41. |
| |
| 作者姓名： | 周剑 宋汉周 |
| |
| 作者单位： | 河海大学地质及岩土工程系,南京,210098 |
| |
| 摘 要： | 本文应用改进的BP网络模型定量分析坝基扬压力的影响因子,赋于网络不同的权值来表示网络的输入变量(水位、温度、时效等因子)对网络的输出变量(扬压力)的影响程度,从而确定各影响因子分量对扬压力的影响比例。采用Levenberg Marquardt算法训练网络,网络达到一定的次数后收敛。实例计算结果表明,该模型具有计算精度高、简便实用等特点。因而认为,把神经网络模型应用于探讨诸如环境量对于效应量影响程度的一类问题,具有好的前景。
|
| 关 键 词： | BP网络模型 坝基扬压力 影响因子 量化分析 |
| 文章编号： | 1000-3665(2005)03-0038-04 |
| 修稿时间： | 2004年6月16日 |
Application of improved neural network model into analysis of uplift pressure under dam-foundation |
| |
| ZHOU Jian,SONG Han-zhou.Application of improved neural network model into analysis of uplift pressure under dam-foundation[J].Hydrogeology and Engineering Geology,2005,32(3):38-41. |
| |
| Authors: | ZHOU Jian SONG Han-zhou |
| |
| Abstract: | In general,there are lots of factors leading to the variation in uplift pressure under a dam foundation.As a result,it is difficult to use a existing model to describe the pressure.Neural network model is a new model with certain advantages compared to other models.In this paper,the improved BP model was used to quantify the factors which affect the variation in the uplift pressure under the dam foundation,illustrated by a case study.The results demonstrate that the improved BP model is characterized by high accuracy and convenience in use.Therefore,it is believed that application of the improved BP model to problems with a cause-effect will be prospective in the future. |
| |
| Keywords: | improved BP model uplift pressure under dam foundation factors quantified analysis |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
