Nonlinear grey prediction model with convolution integral NGMC \((1,n)\) and its application to the forecasting of China's industrial \(SO_2\) emissions
type: Item
instance of: scholarly article
title: Nonlinear grey prediction model with convolution integral NGMC \((1,n)\) and its application to the forecasting of China's industrial \(SO_2\) emissions
zbMATH Open document ID: 1406.86039
DOI: 10.1155/2014/580161
author: Zhengxin Wang
published in: Journal of Applied Mathematics
publication date: 2019-02-01T00:00:00Z
review text: Summary: The grey prediction model with convolution integral GMC \((1, n)\) is a multiple grey model with exact solutions. To further improve prediction accuracy and describe better the relationship between cause and effect, we introduce nonlinear parameters into GMC \((1, n)\) model and additionally apply a convolution integral to produce an improved forecasting model here designated as NGMC \((1, n)\). The model solving process applied the least-squares method to evaluate the structure parameters of the model: convolution was used to obtain an exact solution with this improved grey model. The nonlinear optimisation took the parameters as the decision variables with the objective of minimising forecasting errors. The GMC (1, 2) and NGMC (1, 2) models were used to predict China's industrial \(SO_{2}\) emissions from the basis of the economic output level as the influencing factor. Results indicated that NGMC (1, 2) can effectively describe the nonlinear relationship between China's economic output and \(SO_{2}\) emissions with an improved accuracy over current GMC (1, 2) models.
Mathematics Subject Classification ID: 86A32
Mathematics Subject Classification ID: 62M20
zbMATH DE Number: 7010684
Wikidata QID: Q59053601
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/580161
OpenAlex ID: W2030886099
description: scientific article; zbMATH DE number 7010684