神经网络组合模型在大坝位移预测中的应用

Application of Neural Network Combination Model for Dam Displacement Forecasting

为了提高大坝位移量预测的精度,引入了粒子群优化神经网络的组合预测方法。该组合预测方法以灰色GM(1,1)、回归分析法的预测值及预测结果的平均值作为输入,实际的大坝位移量作为输出,进行非线性组合。实例表明,粒子群优化神经网络组合预测法的均方误差为1.194 6 mm,平均绝对误差为0.781 4 mm,均小于单一模型及等权平均模型的相应值,适用于大坝位移量的预测。

In order to improve the accuracy of dam displacement prediction, a combination forecasting method is applied of particle swarm optimization-neural network. In this method, the dam displacement predictions and the average forecast results of grey GM （ 1,1 ） and regression analysis are used as the network inputs, and the actual displacement value as the outputs to carry out nonlinear combination. The example shows that the mean square er- ror, average absolute error are 1. 194 6 mm and 0. 781 4 mm of the particle swarm optimization-neural network combination forecasting method, less than the corresponding prediction error of a single model and the equal weight method, and is suitable for the dam displacement prediction.