Earticle

초록

영어

In this paper, an improved BP neural network model is proposed. In the model, the momentum factor can improve the training speed and avoid falling into local minimum. Steepness factor and adaptive learning rate can improve the convergence speed. The genetic algorithm is used to solve the problem of low training speed, low accuracy of prediction and easy to fall into local minimum of BP neural network. Then the improved BP neural network model is established to predict GDP of Anhui province. The result shows that it is better than the other models which are presented in this paper on forecasting GDP of Anhui province.

목차

Abstract
 1. Introduction
 2. Optimized BP neural network
  2.1. The principle of BP neural network
  2.2. Improved BP neural network
 3. Model construct and prediction
 4. Conclusion
 References

키워드

저자

• Zhikun Xu [ College of Finance, Hebei Normal University of Science & Technology, Qinhuangdao, Hebei, P.R.China,066004 ]
• Xiaodong Wang [ College of Finance, Hebei Normal University of Science & Technology, Qinhuangdao, Hebei, P.R.China,066004 ]
• Yingying Jin [ College of Finance, Hebei Normal University of Science & Technology, Qinhuangdao, Hebei, P.R.China,066004 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605