考虑混沌成分影响的高边坡位移监控预测模型

Monitoring Model for High Slope Displacement Considering the Effects of Chaos

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 re- alization 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 mod- el 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.