魏博文, 熊威, 李火坤, 彭圣军, 徐镇凯. 融合混沌残差的大坝位移蛙跳式组合预报模型[J]. 武汉大学学报 ( 信息科学版), 2016, 41(9): 1272-1278. DOI: 10.13203/j.whugis20140570
引用本文: 魏博文, 熊威, 李火坤, 彭圣军, 徐镇凯. 融合混沌残差的大坝位移蛙跳式组合预报模型[J]. 武汉大学学报 ( 信息科学版), 2016, 41(9): 1272-1278. DOI: 10.13203/j.whugis20140570
WEI Bowen, XIONG Wei, LI Huokun, PENG Shengjun, XU Zhenkai. Dam Deformation Forecasting of Leapfrog Combined Model Merging Residual Errors of Chaos[J]. Geomatics and Information Science of Wuhan University, 2016, 41(9): 1272-1278. DOI: 10.13203/j.whugis20140570
Citation: WEI Bowen, XIONG Wei, LI Huokun, PENG Shengjun, XU Zhenkai. Dam Deformation Forecasting of Leapfrog Combined Model Merging Residual Errors of Chaos[J]. Geomatics and Information Science of Wuhan University, 2016, 41(9): 1272-1278. DOI: 10.13203/j.whugis20140570

融合混沌残差的大坝位移蛙跳式组合预报模型

Dam Deformation Forecasting of Leapfrog Combined Model Merging Residual Errors of Chaos

  • 摘要: 针对大坝位移监控统计分析中拟合残差序列内蕴的混沌成分,以及传统算法在挖掘监测信息时的优势单一性,结合常规优化算法特点,运用蛙跳算法(shuffled frog leaping algorithm,SFLA)在确定子模型最优权重的基础上,建立了基于SFLA的大坝位移组合预报模型。考虑到统计分析中拟合残差序列的混沌特性,利用相空间重构及混沌理论对位移残差序列值进行分析与预测,并将其残差预测项与SFLA组合模型预测值进行叠加,据此,提出了一种融合混沌残差的大坝位移蛙跳式组合预报方法,并研制了其考虑混沌残差的大坝位移蛙跳算法的实施程序。实例分析表明,所建立的模型与方法相对传统方法在拟合精度及收敛速度方面较优,其模型预报能力有明显提升,这也为其他水工建筑物安全监控模型中位移等效应量的预报分析提供了新方法。

     

    Abstract: There are advantages inanalysis of theintrinsic chaotic component in the fitting residuals in displacement monitoring statistics as well as in traditional algorithms for mining dam monitoring information. This paper therefore combines the characteristics of the conventional optimization algorithm, based on using frog leaping algorithm (SFLA) to determine the optimum weight in the sub-model, to establisha dam displacement combination monitoring model based on SFLA. Taking the chaotic characteristics of fit residuals in the statistical analysis into account through using phase space reconstruction and chaos theory, we analyzed displacement residuals and predicted values, and superimposed the forecast residual term with SFLA model predictions and developed leapfrog algorithm combination forecasting methods fusing chaos residuals, and a dam displacement leapfrog algorithm implementation process that considers of the chaotic residuals. Examples show that the forecasting ability of this model provides a new, improved approach to the analysis of dam deformation.

     

/

返回文章
返回