Earticle

초록

영어

The prediction of chaotic time series is an important research issue. To improve the prediction accuracy, a hybrid approach called WNN-PSO is proposed, which based on the self-learning ability of wavelet neural network, whose parameters are optimized by particle swarm optimization. The WNN-PSO method has higher prediction accuracy, fast convergence, and heightens the ability of jumping the local optimums. The experiment results of the prediction for chaotic time series show the feasibility and effectiveness of the proposed method. Compared with wavelet neural network and BP neural network, the proposed method are superior to them. Finally, the WNN-PSO is applied to predict the life energy consumption of china in our lives.

목차

Abstract
 1. Introduction
 2. Hybrid Model of WNN and PSO
  2.1. Particle swarm optimization
  2.2. Framework of hybrid structure
  2.3. Wavelet neural network improved by PSO
 3. Empirical Results
  3.1. Prediction of Mackey-Glass Time Series
  3.2. Real-world application prediction of life energy
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Hui Li [ College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Department of Information Technology, Jinling Institute of Technology 99 Hongjing Ave., NanJing ]
• Dechang Pi [ College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics 29 Yudao Street, Baixia District, NanJing ]
• Min Jiang [ College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics 29 Yudao Street, Baixia District, NanJing ]

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