首页> 外文期刊>Applied Mathematical Modelling >Multi-parameter grey prediction model based on the derivation method
【24h】

Multi-parameter grey prediction model based on the derivation method

机译:基于衍生方法的多参数灰色预测模型

获取原文
获取原文并翻译 | 示例

摘要

In this study, in order to reduce the morbidity and improve the structural stability of the existing grey multivariable convolution forecasting model, a new derived multivariable grey model based on the derivation method, abbreviated as DMGM (1, n), is presented. Firstly, the time response formula of DMGM (1, n) is deduced by derivation method, which can avoid solving the inverse matrix so as to reduce the morbidity of the model. Secondly, the parameter identification of the model is given based on the least-squares method. Then, it is proved theoretically that DMGM (1, n) is superior to GMC (1, n) because the solution of the former overcomes the shortcoming of the latter that the original model does not take full advantage of all the information from the raw data for modeling. Finally, three real cases with different variables were performed. The fitting and prediction results indicate that DMGM (1, n) is better than GMC (1, n) and the other multivariate grey prediction models in these cases, which also demonstrates that this novel model outperforms the other grey models in this paper.
机译:在本研究中,为了降低发病率并提高现有灰色多变量卷积预测模型的结构稳定性,提出了一种基于推导方法的新推出的多变量灰度模型,缩写为DMGM(1,N)。首先,通过推导方法推断DMGM(1,N)的时间响应公式,其可以避免求解逆矩阵,以降低模型的发病率。其次,基于最小二乘法给出了模型的参数识别。理论上,从理论上证明,DMGM(1,N)优于GMC(1,N),因为前者的解决方案克服了后者的缺点,原有模型不会充分利用原始的所有信息建模数据。最后,执行三种具有不同变量的真实情况。拟合和预测结果表明DMGM(1,N)比在这些情况下比GMC(1,N)和其他多变量灰色预测模型更好,这也表明该新型模型在本文中优于其他灰色模型。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号