Earticle

초록

영어

The relationship between the nuclear parameters and model performance is complex, which is from relevance vector machine (RVM) regression model based on Gaussian radial basis kernel function. Aiming at the problem of how to determine the kernel parameters of RVM, a method to selecting kernel parameter of RVM based on AIC criterion is proposed. Firstly, a novel of statistic Q is proposed based on “Akaike” Information Criterion (AIC), while the Q is as a fitness function. Secondly, we use the differential evolution algorithm (Differential Evolution Algorithm, DE) to find the best kernel parameter, in order to choose determine the kernel parameters. Finally, a RVM regression model mode is established and it is used in predicting gold price. Experimental results show that the prediction model has higher precision and better fitting the generalization ability than the traditional method, which demonstrates the AIC-based criteria for selecting RVM kernel parameter method is effective and feasible.

목차

Abstract
 1. Introduction
 2. Problem Model
 3. The Proposed Algorithm
  3.1. Fitness Function
  3.2. RVM Kernel Parameter Optimization Algorithm Based on AIC
 4. Experiments and Analysis
  4.1. The Experimental Data and the Environment
  4.2. Predicted Results and Analysis of Different Fitness Function
  4.3. Comparative Analysis of Similar Models
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Huang Ming [ Department of Economic Management, Hunan Radio and TV University, Changsha Hunan, 410004 China ]
• Hu Shuyu [ Department of Network Education, Hunan Radio and TV University, Changsha Hunan, 410004 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