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Modified hybrid forecast model considering chaotic residual errors for dam deformation

机译:考虑混沌残余误差的大坝变形修正混合预测模型

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摘要

Dam deformation is double effected by the internal and external environments, showing high nonlinear characteristics. Information mining of dam prototype observation data and effective components contained in the residual sequence is limited in conventional dam deformation forecast models. In order to fully explore the complex nonlinear relationship between dam deformation and environmental factors, modified hybrid forecast model considering chaotic residual errors is proposed on the combination of shuffled frog leapfrog algorithm and chaos theory in this study. Hybrid models are established on the basis of dam deformation prototype observation data, and modified hybrid forecast model is established by determining the optimal weight of each hybrid model with shuffled frog leapfrog algorithm. Considering the chaotic characteristics contained in the residual sequence of modified hybrid forecast model, residual sequence is analyzed and forecasted by chaos theory. By superimposing the residual forecast value with the forecast value of modified hybrid forecast model, modified hybrid forecast model considering chaotic residual errors is established. Example shows that, compared with conventional models, the proposed model is better in fitting precision and convergence speed, and forecast capability is significantly improved by considering the effective components contained in the residual sequence, which presents a new method of the deformation forecast for other hydraulic structures.
机译:大坝变形受到内部和外部环境的双重影响,表现出很高的非线性特性。在常规大坝变形预测模型中,对大坝原型观测数据和残差序列中包含的有效成分的信息挖掘受到限制。为了充分探究大坝变形与环境因素之间复杂的非线性关系,本研究结合改组蛙蛙算法和混沌理论,提出了一种考虑混沌残差的修正混合预测模型。在大坝变形样机观测数据的基础上建立混合模型,并通过改组蛙跳算法确定各混合模型的最优权重,建立改进的混合预测模型。考虑到改进的混合预测模型残差序列中包含的混沌特性,利用混沌理论对残差序列进行了分析和预测。通过将残差预测值与修正的混合预测模型的预测值相叠加,建立了考虑混沌残差的修正的混合预测模型。实例表明,与常规模型相比,该模型在拟合精度和收敛速度上都有较好的提高,并且通过考虑残差序列中包含的有效分量,显着提高了预测能力,为其他液压系统的变形预测提供了一种新方法。结构。

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