Estimating a civil aircraft’s development cost with a GM(1,<i>N</i>) model and an MLP neural network

Xie, Naiming; Yin, Song-Ming; Hu, Chuan-Zhen

doi:10.1108/gs-11-2016-0049

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…In the model prediction, we can calculate the grey correlation of the characteristic behaviour sequence and the correlation sequences, first (Zhang et al , 2017). Then we can select some influence factors with grey correlation degree to model (Xie et al , 2017).…”

Section: Theory and Methodsmentioning

confidence: 99%

Application of combined model with DGM(1,1) and linear regression in grain yield prediction

Yang

2018

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Purpose The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations. Design/methodology/approach Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values. Findings The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction. Practical implications The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed. Originality/value This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

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Section: Theory and Methodsmentioning

confidence: 99%

Application of combined model with DGM(1,1) and linear regression in grain yield prediction

Yang

2018

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“…This paper uses the grey differential equation of an improved GM (1, N) model for parameter estimation and gives a parameter estimation method with optimized background value. For the time response equation of conventional GM (1, N) model, there is generally no analytical solution, and researchers generally use an approximation method [44][45][46][47][48]. This paper gives a scientific solving method for the time response equation of the improved model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)

Cheng

Liu

et al. 2020

Sustainability

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Forecasting China’s clean energy consumption has great significance for China in making sustainably economic development strategies. Because the main factors affecting China’s clean energy consumption are economic scale and population size, and there are three variables in total, this paper tries to simulate and forecast China’s clean energy consumption using the grey model GM (1, 3). However, the conventional grey GM (1, N) model has great simulation and forecasting errors, the main reason for which is the structural inconsistency between the grey differential equation for parameter estimation and the whitening equation for forecasting. In this case, this paper improves the conventional model and provides an improved model GM (1, N). The modeling results show that the improved grey model GM (1, N) built with the method proposed improves simulation and forecasting precision greatly compared with conventional models. To compare the model with other forecasting models, this paper builds a grey GM (1, 1) model, a regression model and a difference equation model. The comparison results show that the improved grey model GM (1, N) built with the method proposed shows simulation and forecasting precision superior to that of other models as a whole. In the final section, the paper forecasts China’s clean energy consumption from 2019 to 2025 using the improved grey model GM (1, N). The forecasting results show that, by 2025, China’s clean energy consumption shall reach the equivalent of 1.504976082 billion tons of standard coal. From 2019 to 2025, clean energy consumption shall increase by 11.32% annually on average, far above the economic growth rate, indicating China’s economic growth shall have a great demand for clean energy in the future. Studies have shown that China’s clean energy consumption shall increase rapidly with economic growth and population increase in the next few years.

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“…However, when the time response formula of the model is solved, it is required to assume that the variation range of each factor variable after the first-order cumulative generation is very small, which is not common in reality, so the simulative and predictive accuracy of the model is not high in most cases for practical problems. Since then, many scholars have made improvements to the model (Luo et al, 2009;Wang, 2014;Tien, 2012;Wang and Hao, 2016;Bolos et al, 2016;Xie et al, 2017;Wang, 2018). Although some achievements have been made, it has not truly solved the inaccurate problem of the time response formula.…”

Section: Introductionmentioning

confidence: 99%

Analysing the high-tech industry with a multivariable grey forecasting model based on fractional order accumulation

Zeng¹

2019

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Purpose High-tech industries play an important role in promoting economic and social development. The purpose of this paper is to accurately predict and analyze the output value of high-tech products in Guangdong Province, China, by using a multivariable grey model. Design/methodology/approach Based on the principle of fractional order accumulation, this study proposes a multivariable grey prediction model. To further enhance the prediction ability and accuracy of the model, an optimized model is established by reconstructing the background value. The optimal parameters are solved by minimizing the average relative error of the system characteristic sequence with the constraint of parameter relationships. Findings The results from the study show that the two proposed models exhibit better simulation and prediction performance than the traditional models, while the optimized model can significantly improve the modelling precision. In addition, it is predicted that the output value of high-tech products is 12,269.443bn yuan in 2021, which will approximately double from 2016 to 2021. Research limitations/implications The two proposed models can be used to forecast the trend of the system and are grown as an effective extension and supplement of the traditional multivariable grey forecasting models. Practical implications The forecast and analysis of the development prospects of high-tech industries would be useful for the government departments of Guangdong Province and professional forecasters to grasp the future of high-tech industries and formulate decision planning. Originality/value A new multivariable grey prediction model based on fractional order accumulation and its optimized model obtained by reconstructing the background value, which can improve the modelling accuracy of the traditional model, is proposed in this paper.

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Estimating a civil aircraft’s development cost with a GM(1,N) model and an MLP neural network

Cited by 9 publications

References 14 publications

Application of combined model with DGM(1,1) and linear regression in grain yield prediction

Application of combined model with DGM(1,1) and linear regression in grain yield prediction

Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)

Analysing the high-tech industry with a multivariable grey forecasting model based on fractional order accumulation

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