基于混沌神经网络理论的城市地面沉降量预测模型

李峰; 宋建军; 董来启; 张先哲; 武艳丽; 张玲

基于混沌神经网络理论的城市地面沉降量预测模型

1.
河南省煤田地质局资源环境调查中心郑州 450002;
2.
河南省煤炭地质勘察研究院郑州 450052

详细信息

作者简介:
李峰主要从事地质灾害及环境影响评价领域的研究.Email:sdlifeng0616@163.com

中图分类号: P642.26
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- 被引次数: 0
出版历程
- 收稿日期: 2007-12-27
- 修回日期: 2008-04-21
- 刊出日期: 2008-10-30

CHAOS NEURAL NETWORK THEORY BASED MODEL FOR QUANTITATIVE PRED ICTION OF URBAN GROUND SUBS IDENCE

1.
Resources and Environm ent investigation Center of Henan Provinicial Coalf ield Geology B ureau, Zhenzhou 450003;
2.
Henan Provincial Coal Geological Exploration Institute| Zhenzhou 450052

摘要

摘要: 通过分析城市地面沉降量时间序列的非线性动力学系统,认为该时间序列具有混沌特性。在此基础上,通过相空间重构的方法建立了用于城市地面沉降量预测的混沌神经网络模型;并利用此模型对高桥地面沉降量进行了预测,并和实际监测沉降量进行了比较,最大绝对预测误差为1. 7,预测的平均误差为0. 0833,研究结果表明,应用混沌神经网络模型进行城市沉降预测是可行、精确的。
- 城市地面沉降 /
- 混沌 /
- 时间序列 /
- 混沌神经网络 /
- 高桥
Abstract: urban ground subsidence is of nonlinear dynamic character and its quantity in time series is analyzed in this paper. Then it is assumed that there is chaos in the urban ground subsidence in time series. Based on this as2 sump tion and using the chaos neural network theory, a p rediction model of urban ground subsidence quantity was built with phase space reconstruction. The ground subsidence quantity in Gao - qiao analyzed and p redicted with thismode1. The observed data are compared with the p redicted data. The largest absolute p rediction error is 1. 7 and the average forecast error is 0. 0833. The results indicate that the chaos neural network theory is reasonable and accurate to p redict the urban ground subsidence.
- Urban ground subsidence /
- Chaos /
- Time series /
- Chaos neural network model /
- Gaoqiao

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参考文献(1)

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