Earticle

초록

영어

An approach based on online RBFNN is proposed to predict the ball-carrier's behavior shooting, passing and dribbling in basketball matches. In order to describe the factors affecting the behavior of ball carrier, artificial potential field (APF)-based player information is introduced to model the court situation of all players after tracking and vision range determination, then a feature vector is formed as the input of the online RBF neural network. The behavior prediction of the ball carrier is solved by the online RBF neural network based on GIRAN learning algorithm. Compared with the offline RBF neural network, the online neural network can adjust both structure and parameters to basketball matches, thus the prediction accuracy is improved to some extent.

목차

Abstract
 1. Introduction
 2. Player Information Amount based on Artificial Potential Field
  2.1. Artificial Potential Field
  2.2. Player Information Quantity based on Artificial Potential Field
 3. Behavior Prediction of the Player by Online RBFNN based on GIRAN
  3.1. RBF Neural Network
  3.2. Prediction of Ball Player
 4. Experimental Analysis and Results
 5. Conclusion
 References

키워드

저자

• Shengbo Liao [ Beijing Jiaotong University, Beijing 100000, china ]
• Deming Zhang [ Heilongjiang University of Chinese Medicine, Harbin 150000, China ]
• Haitao Yang [ Beijing University of Technology, Beijing 100000, china ] Corresponding Author

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