小数据量情境下的滑坡位移非线性变化预测模型

为得到小数据量情境下滑坡位移非线性变化趋势的准确估计,融合广义回归神经网络学习速度快、预测精度高和p GM(1,1)模型减小数据随机性并能增强规律性的建模优势,建立了基于p GM(1,1)模型和广义回归神经网络的滑坡位移组合预测模型.两个工程实例与以往研究结果的对比结果,验证了所建模型可行、有效.

Prediction Model to the Slope Displacement Nonlinear Changing Under the Small-Data Situation

In order to get an accurate estimate of the landslide displacement nonlinear changing trends under the small-data situation,a model based on the p GM( 1,1) and generalized regression neural network was proposed. The new model integrates the advantages of the generalized regression neural network learning speed,high prediction accuracy and the p GM( 1,1) model of reducing of the data randomness and enhancing the regularity. Comparing two engineering examples using the new model with the results of the previous studies,it was shown that the new method was feasible and effective.