Earticle

초록

영어

Study the road traffic carbon emission and accurately predict the problems, the road traffic carbon emission has the complex systems of chaos and nonlinearity, the traditional method ignores the chaos of the road traffic carbon emission change, and it is so difficult to precisely control the rules of the road traffic carbon emission change that the precision of the of traffic carbon emission prediction is lower. In this study, it proposes the road traffic carbon emission prediction model based on the chaos theory and neural network and improves the prediction precision of the road traffic carbon emission time sequence. First of all, it reconstructs the time sequence data of the road traffic carbon emission change through the space, and sorts out the chaos change rules hidden in the time sequence data and then uses the BP neural network to study and carry out the modeling of the time sequence data of the road traffic carbon emission, and optimize the neural network parameter in order to improve the prediction precision of the road traffic carbon emission time sequence. The simulation result shows that, Chao-BPNN has overcome the deficits of the traditional method and could precisely and comprehensively reflect the change rules of the road traffic carbon emission time sequence, and effectively improved the prediction precision of the road traffic carbon emission.

목차

Abstract
 1. Introduction
 2. Prediction Theoretical Framework of Road Traffic Carbon Emission Prediction
  2.1 Prediction Principles of Road Traffic Carbon Emission
  2.2 Difficulty Analysis of Road Traffic Carbon Emission Prediction
 3. Construction of Road Traffic Carbon Emission Prediction Model
  3.1 Linear Data of Nonlinear Road Traffic Carbon Emission Change
  3.2 Data Mining of Chaos Properties of Road Traffic Carbon Emission
  3.3 Construction of Road Traffic Carbon Emission Prediction
  3.4 Road Traffic Emission Prediction Processes of Chaos Theory and Neural Network
 4. Simulation Study
  4.1 Simulation Data
  4.2 Evaluation Standard and Contrast Model
  4.3 Determinations of the Optimal Delay-Time and Embedding Dimensions
  4.4 Result and Analysis
 5. Conclusion
 Acknowledgments
 References

키워드

저자

• Hao Wu [ College of Economics and Management, Nanjing University of Aeronautics and Astronautics ]
• Xianglian Zhao [ College of Economics and Management, Nanjing University of Aeronautics and Astronautics ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605