考虑混沌成分影响的高边坡位移监控预测模型
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摘要
高边坡系统演化过程表现出复杂的非线性动力学特性。有效分离去除噪声,考虑混沌成分对测值序列整体数值特征的影响,是提高高边坡位移监控模型拟合和预测精度的关键问题之一。在对高边坡位移与影响因素相关分析的基础上,基于高边坡系统演化过程中的非线性动力学特性,组合应用相空间重构、小波分析等数值分析手段,研究了高边坡混沌特性提取的实现方法,探讨了考虑混沌成分影响的位移构建原理与算法。该模型重点依据实时监测资料,考虑的是包含混沌成分的动力系统特性,因而可以有效提高监控模型的拟合和预测精度。
Displacement monitoring values exhibit complex nonlinear properties.Among the key problems in improving the fitting and prediction precision of the high slope displacement monitoring model include effectively eliminating noise and consider the effect of chaotic on the numerical characteristics of the overall series of measurements.This paper studies the realization method of high slope displacement chaotic characteristics extraction,based on the correlation analysis between high slope displacement and the affecting factors,in addition to the nonlinear dynamic behavior in the high slope system evolution process,which combines phase space reconstruction,wavelet analysis,and other numerical analysis methods.Furthermore,the current work discusses the building principle,with consideration for dynamic structure chaotic effects.The proposed model depends on monitoring data,and considers dynamic system characteristics which includes chaotic component.Therefore,the fitting and prediction precision of the high slope displacement monitoring model can be effectively improved.
Displacement monitoring values exhibit complex nonlinear properties.Among the key problems in improving the fitting and prediction precision of the high slope displacement monitoring model include effectively eliminating noise and consider the effect of chaotic on the numerical characteristics of the overall series of measurements.This paper studies the realization method of high slope displacement chaotic characteristics extraction,based on the correlation analysis between high slope displacement and the affecting factors,in addition to the nonlinear dynamic behavior in the high slope system evolution process,which combines phase space reconstruction,wavelet analysis,and other numerical analysis methods.Furthermore,the current work discusses the building principle,with consideration for dynamic structure chaotic effects.The proposed model depends on monitoring data,and considers dynamic system characteristics which includes chaotic component.Therefore,the fitting and prediction precision of the high slope displacement monitoring model can be effectively improved.
引文
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[14]Fraser A M.Information and entropy in strange attractors[J].IEEE trans inform theory,1989,35(2):245-262.
[15]Wales D J.calculating the rate of information from chaotictime series by forcasting[J].Nature,1991,350(6318):485-488.
[16]Mallat S.A theory for multiresolution signal decomposition:the wavelet representation.IEEE Trans on PAMI,1989,11(7):674-693.
[17]Farmer J D,Sidorowich J J.Predicting chaotic time series[J].Phys.Rev.Lett.,1987,59(8):845-848.
[18]Wolf A,Swift J B,Swinney H L,et al.Determing Lya-punov exponents from a time series[J].Physica D,1985,16:285-317.
[19]谈小龙,徐卫亚,等.高边坡变形非线性时变统计模型研究[J].岩土力学,2010,31(5):1633-1650.
[2]付义祥,刘志强.边坡位移的混沌时间序列分析方法及应用研究[J].武汉理工大学学报(交通科学与工程版),2003,27(4):473-476.
[3]吴中如,潘卫平.应用Lyapunov指数研究岩土边坡的稳定判据[J].岩石力学与工程学报,1997,16(3):217-223.
[4]靳晓光,李晓红.边坡变形模拟预测的普适灰色模型[J].中国地质灾害与防治学报,2001,12(2):51-55.
[5]汪孔政.时变参数PGM(1,1)变形预测模型及其应用[J].武汉大学学报(信息科学版),2005,30(5):456-459.
[6]徐卫亚,蒋晗,谢守益,等.三峡永久船闸高边坡变形预测人工神经网络分析[J].岩土力学,1999,20(2):27-31.
[7]Moody J,Darken C.Fast learning in networks of locally-tuned processing units.Neural Computation,1989,1(2):281-294.
[8]张建华.边坡坍塌预测的模糊最优理论模型[J].地下空间,1998,18(3):141-147.
[9]秦鹏,秦植海.基于分形理论的岩质高边坡监测资料分析[J].水利水运工程学报,2008,9(3):92-97.
[10]郑东健,顾冲时,吴中如.边坡变形的多因素时变预测模型[J].岩石力学与工程学报,2005,24(17):3180-3184.
[11]贾乃文.粘塑性力学及工程应用[M].北京:地震出版社,2000.
[12]VOIGHT B.A relation to describe rate-dependent materialfailure[J].Science,1989,243:200-203.
[13]SAITO M.Forecasting the time of occurrence of slopefailure[C].Proceedings of 6th International Congress ofSoil Mechanics and Foundation Engineering.Montreal:[s.n.],1965:537-541.
[14]Fraser A M.Information and entropy in strange attractors[J].IEEE trans inform theory,1989,35(2):245-262.
[15]Wales D J.calculating the rate of information from chaotictime series by forcasting[J].Nature,1991,350(6318):485-488.
[16]Mallat S.A theory for multiresolution signal decomposition:the wavelet representation.IEEE Trans on PAMI,1989,11(7):674-693.
[17]Farmer J D,Sidorowich J J.Predicting chaotic time series[J].Phys.Rev.Lett.,1987,59(8):845-848.
[18]Wolf A,Swift J B,Swinney H L,et al.Determing Lya-punov exponents from a time series[J].Physica D,1985,16:285-317.
[19]谈小龙,徐卫亚,等.高边坡变形非线性时变统计模型研究[J].岩土力学,2010,31(5):1633-1650.
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