Water inflow forecasting using the echo state network: A Brazilian case study

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn , Issue , 2007, Pages 2403-2408

  Sacchi, Rodrigo ^a   Ozturk, Mustafa C ^b   Príncipe, José C ^b   Carneiro, Adriano A F M ^a   Da Silva, Ivan N ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b J Crayton Pruitt Family Department of Biomedical Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER NETWORKS; ELECTRIC POWER SYSTEMS; FORECASTING; IMAGE CLASSIFICATION; MATHEMATICAL OPERATORS; RECURRENT NEURAL NETWORKS; REGRESSION ANALYSIS; REINFORCEMENT LEARNING; RESERVOIRS (WATER); THERMOELECTRIC POWER PLANTS; TOPOLOGY; VEGETATION;

ECHO STATE NETWORK; WATER INFLOWS;

NEURAL NETWORKS;

EID: 51749116529     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2007.4371334     Document Type: Conference Paper

References (18)

1. H. Jaeger, "The echo state approach to analyzing and training recurrent neural networks". Technical Report GMD Report 148, German National Research Center for Information Technology, 2001.
2. L. Martinez and S. Soares, "Comparison Between Closed-Loop and Partial Open-Loop Feedback Control Policies in Long Term Hydrothermal Scheduling", IEEE Transactions on Power Systems, vol. 17, no. 2, may 2002
3. G. Kechriotis, E. Zervas, and E. S. Manolakos, "Using recurrent neural networks for adaptive communication channel equalization", IEEE Transactions on Neural Networks, 5(2):267-278, 1994.
4. G. V. Puskorius and L. A. Feldkamp, "Neurocontrol of nonlinear dynamical systems with kalman filter trained recurrent networks". IEEE Transactions on Neural Networks, 5(2):279-297, 1994.
5. A. Delgado, C. Kambhampati and K. Warwick, "Dynamic recurrent neural network for system identification and control". IEEE Proceedings of Control Theory and Applications, 142(4):307-314, 1995.
6. S. Haykin, Neural Networks: A Comprehensive Foundation. Prentice Hall, NJ, second edition, 1998.
7. H. Jaeger and H. Hass, "Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication", Science, 304(5667):78-80, 2004.
8. J. D. Salas, D. C. Boes, and R. A. Smith, "Estimation for arma models with seasonal parameters," Water Resources Res., vol. 18, no. 4, pp. 1006-1010, 1982.
9. A. I. MecLeod, "Diagnostic checking of periodic autoregression, " J. Time Series Anal., vol. 15, no. 2, pp. 221-223, 1994.
10. G. E. P. Box, G. Jenkins, and G. C. Reinsel, Time Series Analysis, Forecasting, and Control. San Francisco, CA: Prentice-Hall, 1994.
11. A. S. Weigend, D. E. Rumelhart, and B. A. Huberman, "Generalization by weight-elimination applied to currency exchange rate prediction," in Proc. IEEE Int. Joint Conf Neural Networks, vol. I, pp. 837-841, 1991.
12. A. F. Atiya, S. M. El-Shoura, S. I. Shaheen, and M. S. El-Sherif, "A comparison between neural-network forecasting techniques - Case study: River flow forecasting," IEEE Tran. Neural Networks, vol. 10, pp. 402-409, Mar. 1999.
13. P. Coulibaly and F. Anctil, "Real-Time Short-Term Natural Water Inflows Forecasting Using Recurrent Neural Networks", International Joint Conference on Neural Networks - IJCNN '99, 1999.
14. M. Figueiredo, R. Ballini, S. Soares, M. Andrade, and F. Gomide, "Learning Algorithms for a Class of Neurofuzzy Network and Application", IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews, vol. 34, no. 3, Aug. 2004.
15. M. Valença, T. Lurdermir and A. Valença, "River Flow Forecasting for Reservoir management through Neural Networks", Proceedings of the Fifth International Conference on Hybrid Intelligent Systems (HIS '05), 2005.
16. R. Sacchi, A. A. F. M. Carneiro and A. F. R. Araújo, "A RBF network trained by the SONARX model and applied to obtain the operation policies of the hydropower systems", Brazilian Symposium on Neural Networks - SBRN, Brazil, 2004.
17. G. A. Barreto and A. F. R. Araújo, "A self-organizing NARX network and its application to prediction of chaotic time series". International Joint Conference on Neural Networks - IJCNN '01, Washington D.C., USA, 2001.
18. J.-S. R. Jang, "ANFIS: Adaptive-Network-based Fuzzy Inference Systems." IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no03: pp. 665-685, 1993.