Abstract
For neural networks, there are local minimum problems and slow convergence speeds. In order to improve the prediction accuracy of the BP neural network prediction model for chaotic time series, the EKF algorithm with BP neural network is used in the field of chaotic time series prediction. Namely, the use of the weight of its output of BP neural network is suitable for the state equation and observation equation of the Kalman filter, which gives the evolution of the Kalman filter algorithm suitable for nonlinear systems. Extended Kalman filter (EKF) algorithmtypical and Mackey-Glass chaotic time series were simulated. The simulation results show that the method of chaotic time series with nonlinear fitting better and higher prediction accuracy.
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References
Zhang, H., Li, R.: Bernstein neural network chaotic sequence prediction based on phase space reconstruction. J. Syst. Simul. 28(4), 880–889 (2016)
Zhang, S., Hu, Y., Bao, H.: Parameters determination method of phase-space reconstruction based on differential entropy ratio and RBF neural network. J. Electron. (China) (S1993-0615) 31(1), 61–67 (2014)
Lee, C.M., Ko, C.N.: Neurocomputing 73, 449 (2009)
Ai, H., Shi, Y.: Study on prediction of haze based on BP neural network. Comput. Simul. 35(1), 402–405 (2015)
Zhang, J., Tan, Z.: Prediction of the chaotic time sevies using hybrid method. Syst. Eng. Theor. Pract. 33(3), 763–769 (2013)
Nie, Y., Wu, J.: An online time series prediction method and its application. J. Beijing Univ. Technol. 43(3), 386–393 (2017)
Li, S., Luo, Y., Zhang, M.: Prediction method for chaotic time series of optimized BP neural network based on genetic algorithm. Comput. Eng. Appl. 47(29), 52–55 (2011)
Zhang, H., Li, R.: Chaotic time sevies prediction of full-parameters continued fraction based on quantum particle sarm optimization algorithm. Control Decis. 31(1), 52–58 (2016)
Hao, J., Tang, D.: Research of run off prediction based on generalized regregression neural network model. Water Resour. Power 34(12), 49–52 (2016)
Acknowledgement
The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported by Initial Scientific Research Fund of FJUT (GY-Z12079), Pre-research Fund of FJUT (GY-Z13018), Fujian Provincial Education Department Youth Fund (JAT170367, JAT170369), Natural Science Foundation of Fujian Province (2018J01640) and China Scholarship Council (201709360002).
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Zhang, XZ., Liu, LS. (2019). Chaotic Time Series Prediction Method Based on BP Neural Network and Extended Kalman Filter. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_11
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DOI: https://doi.org/10.1007/978-3-030-03766-6_11
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