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Study and Application of Slope Displacement Time Series Forecast based on CO-WLSSVM

机译:基于CO-WLSSVM的坡度位移时间序列预测的研究与应用

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A multi-dimension admissible support vector wavelet kernel function is introduced and the model of wavelet least square support vector machine (WLSSVM) is optimized by chaos optimization (CO), which is named as wavelet least squares support vector machine based on chaos optimization (CO-WLSSVM).The optimized model improves the forecasting precision depending multi-dimension interpolation character and sparse change character of the wavelet and quick convergence to the optimum solution of the chaos optimization. The CO-WLSSVM is applied to forecast the displacement of left side bank of slope in first-stage hydroelectric station of Jinping. The result shows that the model possesses higher precision of forecasting.
机译:介绍了多维可允许的支持向量小波核功能,并通过混沌优化(CO)优化了小波最小二乘支持向量机(WLSSVM)的模型,该COOS优化(CO)被命名为基于混沌优化的小波最小二乘支持向量机(CO -WLSSVM)。优化的模型可提高预测精度,取决于小波的多维插值字符和稀疏变化特征,快速收敛到混沌优化的最佳解决方案。 CO-WLSSVM应用于预测金平第一阶段水电站斜坡斜坡的位移。结果表明,该模型具有更高的预测精度。

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