Hou Yue
School of Electronic and Information Engineering, Lanzhou Jiao Tong University, Lanzhou 730070, China
ABSTRACT
In order to improve the neural network structure and setting method of parameters, based on the Particle Swarm Optimization (PSO) and BP Neural Network (BPNN), an algorithm of BP neural network optimized Improved Particle Swarm Optimization (IPSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight so as to compensate the defect of connection weight and thresholds choosing of BPNN, thus BPNN can have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series of Duffing system. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN and BP neural network optimized Particle Swarm Optimization (PSOBPNN), so it is proved that the algorithm is feasible and effective in the chaotic time series.
PDF References Citation
How to cite this article
Hou Yue, 2013. Chaotic Time Series Prediction for Duffing System Based on Optimized Bp Neural Network. Information Technology Journal, 12: 5401-5405.
DOI: 10.3923/itj.2013.5401.5405
URL: https://scialert.net/abstract/?doi=itj.2013.5401.5405
DOI: 10.3923/itj.2013.5401.5405
URL: https://scialert.net/abstract/?doi=itj.2013.5401.5405
REFERENCES
- Kennedy, J. and R. Eberhart, 1995. Particle swarm optimization. Proceedings of the International Conference on Neural Networks, Volume 4, November 27-December 1, 1995, Perth, WA., USA., pp: 1942-1948.
CrossRef - Li, S., Y. Luo and M.R. Zhang, 2011. Prediction method for chaotic time series of optimized BP neural network based on genetic algorithm. Comput. Eng. Appl., 47: 52-55.
Direct Link - Li, M., Y.G. He, S.W. Zhou and W. Tan, 2008. Hybrid genetic neural network method for predicting chaotic time series. J. Syst. Simul., 20: 5825-5828.
Direct Link - Li, M., Y.G. He, S.W. Zhou and W. Tan, 2009. Chaotic time series prediction based on ANFIS with adaptive mutation differential evolution algorithm. Comput. Eng. Appl., 45: 134-137.
Direct Link - Yao, X. and Y. Xu, 2006. Recent advances in evolutionary computation. J. Comput. Sci. Technol., 21: 1-18.
CrossRef - Yang, Y.F., X.M. Ren, W.Y. Qin, W. Ya-Feng and Z. Xi-Zhe, 2008. Prediction of chaotic time series based on EMD method. Acta Phys. Sin., 57: 6139-6144.
Direct Link