Abstract
In this paper, we proposed a novel neural network prediction model based on extreme learning machine for online chaotic time series prediction problems. The model is characterized by robustness and generalization. The initial weights are initialized by orthogonal matrix to improve the generalization performance and the output weights are updated by square root Kalman filter. The convergence of the algorithm is proved by Lyapunov stability theorem. Simulations based on artificial and real-life data sets demonstrate the effectiveness of the proposed model.
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References
Han, M., Wang, Y.N.: Multivariate time series online predictor with Kalman filter trained reservoir. Acta Automat. Sin. 36(1), 169–173 (2010)
Lian, C., Zeng, Z.G., Yao, W., et al.: Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis. Neural Comput. Appl. 24(1), 99–107 (2014)
Ye, Y., Squartini, S., Piazza, F.: Online sequential extreme learning machine in nonstationary environments. Neurocomputing 116, 94–101 (2013)
Wang, X.Y., Han, M.: Improved extreme learning machine for multivariate time series online sequential prediction. Eng. Appl. Artif. Intell. 40, 28–36 (2015)
Zhang, C.Y., Chen, C.L.P., Gan, M., et al.: Predictive deep Boltzmann machine for multiperiod wind speed forecasting. IEEE Trans. Sustain. Energy 6(4), 1416–1425 (2015)
Kasun, L.L.C., Zhou, H., Huang, G.B., et al.: Representational learning with ELMs for big data. IEEE Intell. Syst. 28(6), 31–34 (2013)
Rong, H.J., Huang, G.B., Sundararajan, N., et al.: Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(4), 1067–1072 (2009)
Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice-Hall, Cambridge (2008)
Michel, A.N., Hou, L., Liu, D.R.: Stability of Dynamical Systems: Continuous, Discontinuous, and Discrete Systems. Springer Science Business Media, Boston (2007). doi:10.1007/978-0-8176-4649-3
Li, Y., Tan, Y., Dong, R., et al.: State estimation of macromotion positioning tables based on switching Kalman filter. IEEE Trans. Control Syst. Technol. 25(3), 1076–1083 (2017)
Acknowledgment
This paper is supported by the National Natural Science Foundation of China (61374154) and the Fundamental Research Funds for the Central Universities (DUT16RC(3)123).
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Feng, S., Xu, M., Han, M. (2017). Online Chaotic Time Series Prediction Based on Square Root Kalman Filter Extreme Learning Machine. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_42
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DOI: https://doi.org/10.1007/978-3-319-70093-9_42
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