Study on Chaotic Time Series Prediction Algorithm for Kent Mapping Based on Particle Swarm Optimization

Hong Li Bian

doi:10.4028/www.scientific.net/AMM.511-512.941

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 511-512Study on Chaotic Time Series Prediction Algorithm...

Study on Chaotic Time Series Prediction Algorithm for Kent Mapping Based on Particle Swarm Optimization

Abstract:

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

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Periodical:

Applied Mechanics and Materials (Volumes 511-512)

Pages:

941-944

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.511-512.941

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Online since:

February 2014

Authors:

Hong Li Bian*

Keywords:

BP Neural Network (BPNN), Chaotic Time Series , Kent Mapping, Particle Swarm Optimization (PSO), Prediction

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* - Corresponding Author

References

[1] Y.F. Yang, X.M. Ren, W.Y. Qin, et al: Acta Physica Sinica, Vol. 57 (2008), pp.6139-6144.

Google Scholar

[2] J. Du, Y.J. Cao and Z.J. Liu: Acta Physica Sinica, Vol. 58 (2008), pp.5997-6005.

Google Scholar

[3] S. Li, Y. Luo and M.R. Zhang: Computer Engineering and Applications, Vol. 47(2011), pp.52-55.

Google Scholar

[4] W. You, X. M. Liang, Z.Q. Long, et al: Control and Decision, Vol. 27 (2012), pp.1265-1268.

Google Scholar

[5] J．Kennedy and R．Eberhart: Particle Swarm Optimization, in: Proceedings of IEEE International Conference on Neural Networks (1995).

Google Scholar

[6] H.Y. Wang, G. D. Shi: Artificial neural network technology and its application (China petrochemical press, Beijing2002).

Google Scholar

[7] Z.Q. Chen, Q. Zhou and Z.Z. YUAN: Journal of Systems Science and Mathematical Sciences, Vol. 31 (2011), pp.731-741.

Google Scholar