| 大坝渗流监测遗传神经网络模型 |
| |
| 引用本文： | 王志旺,吴盖化,张漫,张保军. 大坝渗流监测遗传神经网络模型[J]. 水电能源科学, 2003, 21(4): 26-27,34 |
| |
| 作者姓名： | 王志旺 吴盖化 张漫 张保军 |
| |
| 作者单位： | 1. 中国地质大学,研究生院,湖北,武汉,430074
2. 长江科学院,湖北,武汉,430010 |
| |
| 摘 要： | 基于遗传神经网络的基本概念及学习步骤，对大坝坝基渗流量、坝基扬压力监测数据进行了训练和预测。结果表明，利用遗传算法特有的全局优化能力，可以较好地完成网络的学习，而且还减少了网络训练次数，缩短了网络训练时间。
|
| 关 键 词： | 大坝渗流监测 人工神经网络 遗传算法 遗传神经网络模型 全局优化能力 |
| 文章编号： | 1000-7709(2003)04-0026-03 |
Dam Seepage Monitoring Model Based on Genetic Algorithm of Neural Network |
| |
| WANG Zhi-wang WU Gai-hua ZHANG Man ZHANG Bao-jun. Dam Seepage Monitoring Model Based on Genetic Algorithm of Neural Network[J]. International Journal Hydroelectric Energy, 2003, 21(4): 26-27,34 |
| |
| Authors: | WANG Zhi-wang WU Gai-hua ZHANG Man ZHANG Bao-jun |
| |
| Affiliation: | WANG Zhi-wang~1 WU Gai-hua~2 ZHANG Man~2 ZHANG Bao-jun~2 |
| |
| Abstract: | On the basis of brief introduction of the principle and algorithm of genetic neural network, the forcast model is applied on the monitoring data of seepage and hydraulic uplift pressure of a dam foundation. The result indicates that the forcast model could complete the training and forcasting of the network with less training frequency and time by means of overall optimizing ability of genetic algorithm. |
| |
| Keywords: | dam seepage monitoring artifical neural network genetic algorithm forcast |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
