基于伸缩变换不一致性的灰色磨光优化模型

很多预测模型都是利用原始数据直接地代入模型中优化参数,这样仍然很难避免整体数据之间的相互约束,使某些局部误差还是很大.为了克服这些缺点,特提出一种基于伸缩变换不一致性的灰色磨光优化模型,模型通过引入描述局部性质的磨光因子、可逆变换和优化方法,很好地保证整体的拟合精度.通过实例比较,模型比其它几种模型的拟合精度更高.

The Grey Polished Optimization Model Basing on Inconsistency of Stretching Transformation

Many forecast models are used directly to substitute the original data into the model to optimize the parameters of demand,this is still difficult to avoid binding between the overall data,and local error is still very large.In order to overcome these shortcomings,the grey smoothing optimization model basing on inconsistency of stretching transformation is proposed.The model introduces polished factor that describe the partial property、reversible transformation and optimization method,ensuring well the overall fitting accuracy.Compared by example,the fitting accuracy of the model is better than that of several other models.