最优化分数阶算子EGM(1,1)模型在变形监测预报中的应用

Application of Optimized Fractional Order EGM(1,1)Model in Deformation Monitoring and Forecasting

针对传统灰色模型在形变监测中数据序列拟合和预测精度不理想的情况,提出粒子群算法优化的分数阶算子EGM(1,1)模型。通过粒子群算法选择拟合EGM(1,1)平均相对误差最小的分数阶次,构建最优分数阶算子EGM(1,1)模型。用典型的变形监测数据验证优化模型,结果表明,优化模型对变形监测数据的拟合和预测都达到较高的精度,说明优化模型在变形监测数据的处理中具有可行性和有效性。

In view of the unsatisfactory fitting and prediction accuracy of deformation monitoring data series,we propose a fractional order EGM(1,1)model,optimized by particle swarm optimization,to fit and predict deformation monitoring data.We use particle swarm optimization(PSO)to select the fractional order,which fits the minimum average relative error of EGM(1,1),and the optimal fractional order EGM(1,1)model is constructed.We use typical deformation monitoring data to validate the optimization model.The results show that the optimization model achieves high accuracy in fitting and predicting deformation monitoring data.It shows that the optimization model is feasible and effective in processing deformation monitoring data.