%0 Journal Article
%T 邻近的Hermite多项式灰色模型及其应用<br>Adjacent Hermite Polynomial Grey Model and Its Application
%A 池沛
%J Statistics and Applications
%P 103-110
%@ 2325-226X
%D 2022
%I Hans Publishing
%R 10.12677/SA.2022.111012
%X 针对传统灰色模型在建模上忽略对新数据的优先性以及缺乏微调数据的能力，本文引进了r阶邻近的累积生成算子，并且结合Hermite多项式的定义，优化了传统灰色模型的右端结构，构建了邻近的Hermite多项式灰色模型，即AHFGM(1,1)模型。基于中国一次电力及其他能源生产量的数据建立了预测模型，利用灰狼优化算法得到最优参数，与三个经典的灰色模型进行拟合和预测精确度分析，结果表明AHFGM(1,1)模型拥有更优的拟合和预测性能，以及较强的稳定性。最后利用建立的AHFGM(1,1)模型合理地预测了2021~2025年我国一次电力及其他能源生产量的变化趋势。<br />
In view of the fact that the traditional grey model ignores the priority of new data in modeling and lacks the ability to fine-tuneing the data, this paper introduces the r-order adjacent accumulative generator operator, and combines the definition of the Hermite polynomial to optimize the structure of the right end of the traditional grey model. The adjacent Hermite polynomial grey model, the AHFGM(1,1) model is constructed. Based on the data of primary power and other energy production in China, a prediction model is established, and the optimal parameters are obtained by using the grey wolf optimization algorithm. Fitting and prediction accuracy analysis with three classical grey models, the results show that the AHFGM(1,1) model has better fitting and prediction performance, as well as strong stability. Finally, the established AHFGM(1,1) model is used to reasonably predict the change trend of primary electricity and other energy production in my country from 2021 to 2025.
%K r阶邻近的累积生成算子，Hermite多项式，AHFGM(1
%K 1)模型，一次电力及其他能源生产量<br>r-Order Adjacent Accumulative Generator Operator
%K Hermite Polynomial
%K AHFGM(1
%K 1) Model
%K Primary Power and Other Energy Production
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=48642