Earticle

초록

영어

Exhaust contaminant of gasoline vehicles is a crucial aspect to measure the vehicle performances and the air pollutions. According to the feature of vehicles, the emission of exhaust contamination of a vehicle is different as time goes by, which shows an increase tendency in most of the cases. Measuring the changes of a vehicle's exhaust contaminant emission is of great importance in the field of vehicle engineering. However, it is hard to determine and find out the regulations of the emission, needing a long time for regular determination and advanced relevant machines. In this article, we aim at providing two novel methods for the prediction of exhaust contaminant of gasoline vehicles, using grey model GM (1,1) and artificial neural networks (ANNs) models respectively. Results show that both the GM (1,1) model and ANN models are comparatively precise for the prediction. The GM (1,1) model can quickly obtain the predicted values of exhaust contaminant, but it is less precise than ANN models. However, ANN models need more time for the training process, compared to GM (1,1) mo

목차

Abstract
 1. Introduction
 2. Grey Model GM (1,1)
 3. Artificial Neural Network
 4. Results and Discussion
  4.1. Development of Grey Model GM (1,1)
  4.2. Development of ANN Models
 5. Conclusion
 References

키워드

저자

• Jingbin Song [ Department of Automobile Engineering, Changzhou College of Mechatronic Technology, Changzhou, 213164, Jiangsu, China ] Corresponding author
• Shuzhao Li [ Dalian University of Technology, Changzhou, 116024, Liaoning, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605