Application of intelligent prediction to time-effect deformation analysis for the partition frusta of Three Gorges Permanent Shiplock

Lü Ai-zhong; MO Xiao-ming

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Application of intelligent prediction to time-effect deformation analysis for the partition frusta of Three Gorges Permanent Shiplock

更新时间：2026-06-25

- Application of intelligent prediction to time-effect deformation analysis for the partition frusta of Three Gorges Permanent Shiplock
- Vol. 28, Issue 5, (2007)
- 作者机构：
  
  1. 华北电力大学水利水电系,北京,102206
  2. 山东科技大学土木建筑学院,青岛,266510
  3. 富伦电子公司,武汉,430019
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU457
- Online First：10 September 2013，
  
  Published：2007
- 稿件说明：
移动端阅览
Lü Ai-zhong, MO Xiao-ming. Application of intelligent prediction to time-effect deformation analysis for the partition frusta of Three Gorges Permanent Shiplock[J]. 2007, 28(5).
DOI：

Lü Ai-zhong, MO Xiao-ming. Application of intelligent prediction to time-effect deformation analysis for the partition frusta of Three Gorges Permanent Shiplock[J]. 2007, 28(5). DOI：

摘要

人工神经网络通过调整网络中的连接权值、网络结构，实现输入输出非线性映射关系，对三峡永久船闸中隔墩的变形进行预测。具有全局最优化遗传算法可使连接权值、网络结构得到最优解。将遗传算法与神经网络结合起来进行变形演化特征识别，对三峡永久船闸中隔墩时效变形进行智能预测，应用结果表明该方法具有较高的预测精度。

Abstract

The artificial neural network carries out the input and output nonlinear mapping relationship to predict the deformation of the partition frusta of the Three Gorge’s Permanent Shiplock by adjusting the connected weighted value and the network structure. The global optimal solution to the weighted value and the network structure is obtained by the optimized genetic algorithm. Combined the genetic algorithm with neural network

the characteristics of the deformation evolvement is identified. And then the time-effect deformation for the Three Gorges Permanent Shiplock is intelligently predicted; and it is shown that the proposed approach has higher predicting precision.

关键词

Keywords

references

Views

885

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Seepage real-time forecast model based on genetic neural network

Dam deformation forecasting by evolving artificial neural network

Dam deformation monitoring model based on genetic algorithm of neural network

Search of three-dimensional slip surface for slope based on finite element calculation

Back analysis of viscoelasto-plastic constitutive parameters of artificial frozen soil

Related Author

YAN Bin

ZHOU Jing

LI Shou-ju

LIU Ying-xi

LIU Yu-jing

WANG Zhi-wang1

ZHANG Bao-jun2

LI Di2

Related Institution

School of Civil and Hydraulic Engineering, Dalian University of Technology

College of Water Conservancy, Shenyang Agricultural University

State Key Laboratory of Structural Analysis of Industrial Equipment, Dalian University of Technology

Basic Science Department, Changchun University of Technology

China University of Geoscience

AI问答

⁰