원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.6 No.6
2013.11
pp.369-380
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보