Abstract：In order to fill in the gaps of the traditional GM(1,1) power model with equal-weight construction for background values, a non-equidistance linear time-varying parametric GM(1,1) power model with weighted optimization of background values is constructed for the non-equidistance spaced oscillation characteristics of the original deformation sequences. In addition, we use the particle swarm optimization(PSO) algorithm with fast convergence and high precision to solve the power exponent and background value weight. Taking the cumulative settlement observation data of monitoring points in two mining areas as examples, we use the constructed model for settlement analysis and prediction. The results show that average absolute percentage fitting errors of the model in this paper are 2.33% and 4.70% respectively, and the prediction errors are 2.10% and 6.38% respectively, which are better than other three GM(1,1) power models. The engineering application shows that the proposed optimization model has applicability and superiority to deal the small-sample non-equidistant oscillation sequences, and that it is suitable for short-term prediction and time-varying analysis in coal mining deformation monitoring engineering.
WANG Bing,LI Peixian,ZHANG Jun et al. Application of Non-Equidistant Linear Time-Varying Parameter GM(1,1) Power Model with Optimized Background Value in Deformation Monitoring[J]. jgg, 2022, 42(8): 823-828.
