混合灰色预测模型HGM(1,1)

Hybrid grey prediction model HGM(1,1)

研究了现有的传统GM(1,1)(简称GM(1,1))、基于缓冲算子和时间响应函数优化的GM(1,1)、首输入GM(1,1)(简称FGM(1,1))、变权缓冲GM(1,1)(简称VWGM),针对它们存在的问题,提出了一种新的预测模型——混合GM(1,1)(Hybrid GM(1,1),简称HGM(1,1))。该模型引入了粒子群优化算法、变权缓冲算子以及FMG(1,1)模型,并以拟合值与实际值的灰色关联度最大为目标进行参数优选。结果显示,改进后的模型增强了适应性,能够充分利用原始数据信息,提高模型的预测精度,并更好地处理灰色数据。该模型可用于实验数据处理、环境管理、资源管理、城市规划等领域。

The existing traditional GM(1,1)(GM(1,1)),the GM(1,1)based on buffer operator and time response function optimization,the first input GM(1,1)(FGM(1,1)),and the variable weight buffer GM(1,1)(VWGM)were studied.To solve their existing problems,we proposed a prediction model,hybrid GM(1,1)(HGM(1,1)).This model introduces particle swarm optimization algorithm,variable weight buffer operator and FMG(1,1)model and takes the maximum grey correlation between the fitting value and the actual value as the goal to optimize the parameters.After improvement,the adaptability of the model is enhanced,the original data information is fully utilized,the prediction accuracy of the model is improved and the grey data can be better processed.This model could be applied to experimental data processing,environmental management,resource management,and urban planning.