|
|||||
|
|
| 大坝安全诊断的混沌优化神经网络模型 | |
| 引用本文： | 曹茂森,邱秀梅,夏宁.大坝安全诊断的混沌优化神经网络模型[J].岩土力学,2006,27(8):1344-1348. |
| 作者姓名： | 曹茂森 邱秀梅 夏宁 |
| 作者单位： | 1. 河海大学,土木工程学院,南京,210098;山东农业大学,水利土木工程学院,泰安,271018 2. 山东农业大学,水利土木工程学院,泰安,271018 3. 河海大学,土木工程学院,南京,210098 |
| 基金项目： | 中国博士后科学基金;国家自然科学基金 |
| 摘 要： | 为了提高大坝变形的预测精度，采用小波变换和分形理论对大坝位移观测数据的非线性动力学特性进行了分析，揭示了其具有低维混沌动力特性，这为大坝变形预测模型的建立提供了理论依据和先验知识。基于低维混沌动力特性，设计了能捕获大坝位移观测数据全局动力特性，兼具神经网络模型结构优化和动力机制时新的混沌优化神经网络大坝变形预测模型。在工程实例中，由多个度量指标组成量化评价体系，对模型预测性能进行综合评价，结果表明，所建模型比传统BP神经网络和APMA模型具有更高的预测精度。 |
| 关 键 词： | 大坝位移 低维混沌 动力特性 小波变换 混沌优化神经网络 |
| 文章编号： | 1000-7598-（2006）08-1344-05 |
| 收稿时间： | 2004-02-05 |
| 修稿时间： | 2004-02-052004-12-05 |
A chaos-optimized neural network model for dam safety monitoring |
|
| CAO Mao-sen,QIU Xiu-mei,XIA Ning.A chaos-optimized neural network model for dam safety monitoring[J].Rock and Soil Mechanics,2006,27(8):1344-1348. | |
| Authors: | CAO Mao-sen QIU Xiu-mei XIA Ning |
| Institution: | 1. College of Civil Engineering , Hohai University, Nanjing 210098, China; 2. College of Water Conservancy and Civil Engineering, Shandong Agricultural University, Tai’an 271018, China |
| Abstract: | Dam deformation prediction is important for dam safety monitoring and has become a focus of increasing interest in recent years. In this study, on the basis of nonlinear dynamic property analysis of the observations of dam displacements, a novel methodology is proposed to establish dam deformation prediction model with improved prediction precision. Firstly, the dynamic properties of observations of dam displacements are studied by combined wavelet transform with fractal, and the results reveal that dam displacements possess certain low dimensional chaotic character. This provides theoretical foundation and transcendental knowledge for relational establishment of dam deformation prediction model. Moreover, derived from the low dimensional chaotic character, a chaos-optimized neural network model for dam deformation prediction is constructed, which is not only capable of capturing the dynamic properties of observations of dam displacements but also of implementing the model’s structural optimization and dynamic mechanism refreshing. Finally, in the practical application of dam deformation prediction, the model performance is quantificationally assessed by multiple indices. The result demonstrates that chaos-optimized neural network model holds higher prediction precision than the conventional back propagation (BP) neural network and ARMA models; and therefore, it is promising for dam safety monitoring. |
| Keywords: | dam displacements low dimensional chaos dynamic properties wavelet transform chaotic-optimimal neural networks |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |
| 点击此处可从《岩土力学》浏览原始摘要信息 | |
| 点击此处可从《岩土力学》下载免费的PDF全文 | |