Earticle

초록

영어

The observation and forecasting of vessel traffic flow is the foundmental of design for ships’ routeing system. An integrated Genetic Algorithm (GA) based Support Vector Machine (SVM) model for vessel traffic flow forecasting with input factors selection procession is presented in this paper. GA based SVM forecasting model is established whose parameters were optimized through genetic algorithms. Finally, the prediction model is used for ningbo-zhoushan port and the prediction result shows that the improved model reflects the actual growth of vessel traffic flow trend more reasonable and effectively.

목차

Abstract
 1. Introduction
 2. Forecasting Models
  2.1. Support Vector Regression
  2.2. GA-Based Feature Selection and Parameters Optimization of SVR Models
 3. Conclusion and Discussion
 References

키워드

저자

• Hao Zhang [ Engineering Research Center of the shipping simulation, Ministry of Education, Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China ]
• Yingjie Xiao [ Engineering Research Center of the shipping simulation, Ministry of Education, Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China ]
• Xiangen Bai [ Engineering Research Center of the shipping simulation, Ministry of Education, Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China ]
• XiaojunYang [ Engineering Research Center of the shipping simulation, Ministry of Education, Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China ]
• Liang Chen [ Engineering Research Center of the shipping simulation, Ministry of Education, Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605