1Sivakumar B. A Phase-space Reconstruction Approach to Prediction of Suspended Sediment Concentration in Rivers[J]. Journal of Hydrolo- gy, 2002, 258(1-4).
2Jaeger H, Haas H. Harnessing Nonlinearity: Predicting Chaotic Sys- tems and Saving Energy in Wireless Communication[J]. Science, 2004, 304(5667).
3Lukosevi-ius M, Jaeger H. Reservoir Computing Approaches to Re- current Neural Network Training[J]. Computer Science Review, 2009, 3(3).
4Zhou Z H, Yuan J. Medical Diagnosis with C4.5 Rule Preceded by Ar- tificial Neural Network Ensemble[J]. IEEE Transactions on lntorma- tion Technology in Biomedicine, 2003, 7(1).
7De Felice M, Yao X. Short-term load Fforecasting with Neural Net- work Ensembles: A Comparative Study[J]. IEEE Computational Intelli- gence Magazine, 2011, 6(3).
5Ismael Sa'nchez. Short-term prediction of wind energy production[J]. Int J of Forecasting, 2006, 22(1): 43-56.
6Ernst B, Oakleaf B, Ahlstrom M L, et al. Predicting the Wind[J]. IEEE Power and Energy Magazine, 2007, 5(6): 79-89.
7Kennel M B, Brown R, Abarbanel H D I. Determining embedding dimension for phase space reconstruction using a geometrical construction[J]. Physical Review A, 1992, 45(6): 3403-3411.
8Sideratos G, Hatziargyriou N. Using radial basis neural networks to estimate wind power production[C]. IEEE Power Engineering Society General Meeting. Tampa, 2007: 1-7.
9Tang Xiaowo,Zhou Zongfang,Shi Y. The Error Bounds of Combined Forecasting[J].Mathematical and Computer Modelling,2002,36(9).
10Huang KY, Jane CJ.A Hybrid Model for Stock Market Forecasting and Portfolio Selection Based on ARX, Grey System and RS Theories [J].Expert Systems with Applications,2009,36(3).
4JAEGER H. The "echo state" approach to analyzing and training recurrent neural networks [ R]. GMD Report 148, GMD German National Research Institute for Computer Science, 2001.