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

李峰, 宋建军, 董来启, 张先哲, 武艳丽, 张玲. 2008: 基于混沌神经网络理论的城市地面沉降量预测模型. 工程地质学报, 16(5): 715-720.
引用本文: 李峰, 宋建军, 董来启, 张先哲, 武艳丽, 张玲. 2008: 基于混沌神经网络理论的城市地面沉降量预测模型. 工程地质学报, 16(5): 715-720.
LI Feng, SONG Jianjun, DONG Laiqi, ZHANG Xianzhe, WU Yanli, ZHANG Ling. 2008: CHAOS NEURAL NETWORK THEORY BASED MODEL FOR QUANTITATIVE PRED ICTION OF URBAN GROUND SUBS IDENCE. JOURNAL OF ENGINEERING GEOLOGY, 16(5): 715-720.
Citation: LI Feng, SONG Jianjun, DONG Laiqi, ZHANG Xianzhe, WU Yanli, ZHANG Ling. 2008: CHAOS NEURAL NETWORK THEORY BASED MODEL FOR QUANTITATIVE PRED ICTION OF URBAN GROUND SUBS IDENCE. JOURNAL OF ENGINEERING GEOLOGY, 16(5): 715-720.

基于混沌神经网络理论的城市地面沉降量预测模型

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

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

  • 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。
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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

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