A comparison study on electric vehicle growth forecasting based on grey system theory and NAR neural network

Author(s):  
Xian Zhang ◽  
Ka Wing Chan ◽  
Xuesen Yang ◽  
Yangyang Zhou ◽  
Kexin Ye ◽  
...  
2012 ◽  
Vol 204-208 ◽  
pp. 520-525 ◽  
Author(s):  
Rong Yu Li ◽  
Yong Fen Ruan ◽  
Shi Sheng Li ◽  
Yong Hong Wu

The stability of the landslide can be effectively evaluated and predicted by predicting the future development of landslide deformation according to the actual deformation of the landslide. Therefore, the accuracy of the prediction regarding the landslide deformation determines the validity of the landslide stability assessment. The GM (1.1) model in the grey system theory, uses displacement time series to establish the grey differential equation. By solving the equation, we can obtain a time response function, which can then be used to predict the landslide deformation. The BP neural network is a used for training and exercising on the deformed samples. After the error meets the requirement, we can then use the trained model to predict the landslide deformation. This paper use both grey system theory model and BP neural network model to predict Jinlong ditch application field landslide deformation.The prediction results are compared and analyzed to test the accuracy of these two predictions. Finding a more accurate prediction method for application in actual engineering project has practical significance.


2019 ◽  
Vol 10 (9) ◽  
pp. 852-860
Author(s):  
Mahmoud Elsayed ◽  
◽  
Amr Soliman ◽  

Grey system theory is a mathematical technique used to predict data with known and unknown characteristics. The aim of our research is to forecast the future amount of technical reserves (outstanding claims reserve, loss ratio fluctuations reserve and unearned premiums reserve) up to 2029/2030. This study applies the Grey Model GM(1,1) using data obtained from the Egyptian Financial Supervisory Authority (EFSA) over the period from 2005/2006 to 2015/2016 for non-life Egyptian insurance market. We found that the predicted amounts of outstanding claims reserve and loss ratio fluctuations reserve are highly significant than the unearned premiums reserve according to the value of Posterior Error Ratio (PER).


2000 ◽  
Vol 11 (1) ◽  
pp. 34-36 ◽  
Author(s):  
Wang Jing ◽  
Hou Yuesong ◽  
Li Weilin ◽  
Cheng Wenhui

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