References

1. Y.-Y. Meng, J.-P, Lu, Wind power chaos prediction based on Volterra adaptive filter, Power Syst. Prot. Control, 40 (2012) 91–96.
2. Y.-H. Lei, C.-Y. Song, S.-Q. Ding, Nonlinear prediction of underwater reverberation based on a Volterra adaptive filter, J. Harbin Eng. Univ., 10 (2007) 1127–1130.
3. W. Zhang, Q. Huang, C. Tong, Chaotic local-region multi-step forecasting model of flow time series and its application on the upper reach of the Yellow River, J. Hydroelec. Eng., 2007 11–15.
4. X.I. Jiang, C. Liu, Y. Wang, H. Wang, Preliminary study on prediction of annual runoff change of Sanmenxia station in the Yellow River by using mixed regression model, Sci. China Ser. E: Technol. Sci., S1 (2004) 95–102.
5. L.I. Yanbin, Research on chaotic characteristics and forecasting of the river runoff, Xi’an Univ. Technol., (2009) 45–57.
6. Y. Li, Q. Huang, J. Xu, Z. Zhang, Support vector machine model of mid-long term prediction of runoff in stream, J. Hydroelec. Eng., 5 (2008) 28–32.
7. X.Q. Zhang, J. Liang, Chaotic characteristic analysis and prediction model study on wind power time series, Acta Phys. Sinica, 19 (2012) 70–81.
8. M. Han, Theory and Method of Chaotic Time Series Prediction, China Water Power Press, Beijing, (2007) 24–30.
9. X.-Q. Zhang, J. Liang, Chaotic time series prediction model of wind power based on ensemble empirical mode decompositionapproximate entropy and reservoir, Acta Phys. Sinica, 5 (2013) 76–85.
10. J.-S. Zhang, X.-C. Xiao, Predicting low-dimensional chaotic time, Acta Phys. Sinica, 49 (2000) 403–408.
11. Z. Lu, Z. Cai, K. Jiang, Prediction of chaotic time series using singular value decomposition Volterra filter, J. Wuhan Univ. Technol., 4 (2007) 672–675.
12. J.-S. Zhang, X.-C. Xiao, Prediction of chaotic time series by using adaptive higher-order nonlinear Fourier infrared filter, Acta Phys. Sinica, 49 (2000) 1222–1226.