Bernstein Neural Network Chaotic Sequence Prediction Based on Phase Space Reconstruction

Authors

Hongli Zhang, 1. Department of Electrical Engineering, Xinjiang University, Urumqi 830047, China;;
Ruiguo Li, 1. Department of Electrical Engineering, Xinjiang University, Urumqi 830047, China;;
Wenhui Fan, 2. Department of Automation, Tsinghua University, Beijing 100084, China;

Abstract

Abstract: In view of the low prediction accuracy and the complex structure of traditional BP neural network, RBF neural network and AR model, a new prediction method with the combination of phase space reconstruction and Bernstein neural network was proposed, and PSO algorithm was used for parameters optimization of combination forecast model. Taking Sprott-J chaotic system and traffic flow system as models respectively, the combination of autocorrelation and Cao method was used to reconstruct phase space of chaotic time sequence, the refactoring phasor of time delay and Bernstein neural network were used to establish the prediction model, and do comparative analysis with traditional BP neural network, RBF neural network and AR models. The simulation results show that the combination prediction of phase space reconstruction and Bernstein neural network has a simple structure and can get more preferable simulation effect and higher prediction accuracy.

Recommended Citation

Zhang, Hongli; Li, Ruiguo; and Fan, Wenhui (2020) "Bernstein Neural Network Chaotic Sequence Prediction Based on Phase Space Reconstruction," Journal of System Simulation: Vol. 28: Iss. 4, Article 15.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss4/15

First Page

880

Revised Date

2015-04-22

DOI Link

https://doi.org/

Last Page

889

CLC

TP391.9

Recommended Citation

Zhang Hongli, Li Ruiguo, Fan Wenhui. Bernstein Neural Network Chaotic Sequence Prediction Based on Phase Space Reconstruction[J]. Journal of System Simulation, 2016, 28(4): 880-889.