A. Arash and F. Atry, Multi-step ahead forecasts for electricity prices using NARX: A new approach, a critical analysis of one-step ahead forecasts », Energy Conversion and Management, pp.739-747, 2009.

T. G. Barbounis and J. B. Theocharis, A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation, Neurocomputing, vol.70, issue.7-9, pp.1525-1542, 2007.
DOI : 10.1016/j.neucom.2006.01.032

W. Batko, Prediction method in technical diagnostics, 1984.

E. Bernauer, Les réseaux de neurones et l'aide au diagnostic : un modèle de neurones bouclés pour l'apprentissage de séquences temporelles, 1996.

D. G. Bostwichk and H. B. Burke, Prediction of individual patient outcome in cancer, Cancer, vol.6, issue.S8, pp.1643-1646, 2001.
DOI : 10.1002/1097-0142(20010415)91:8+<1643::AID-CNCR1177>3.0.CO;2-I

B. George and J. Gwilym, Time series analysis: Forecasting and control, 1970.

C. Byington, M. Roemer, G. Kacprzynski, and T. Galie, Prognostic enhancements to diagnostic systems for improved condition-based maintenance, IEEE Aerospace Conference, 2002.

C. Cempel, Simple condition forecasting techniques in vibroacoustical diagnostics, Mechanical Systems and Signal Processing, vol.1, issue.1, pp.75-82, 1987.
DOI : 10.1016/0888-3270(87)90084-7

F. Ciarapica and G. Giacchetta, Managing the condition-based maintenance of a combined-cycle power plant: An approach using soft computing techniques, Journal of Loss Prevention in the Process Industries, pp.316-325, 2006.
DOI : 10.1016/j.jlp.2005.07.018

Q. Dai and S. Chen, Chained DLS-ICBP Neural Networks with Multiple Steps Time Series Prediction, Neural Processing Letters, vol.11, issue.1, pp.95-107, 2005.
DOI : 10.1007/s11063-004-7774-7

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.669.6449

D. Gooijer, J. G. Hyndman, and R. J. , 25 years of time series forecasting, International Journal of Forecasting, vol.22, issue.3, pp.443-473, 2006.
DOI : 10.1016/j.ijforecast.2006.01.001

D. Evgueni, R. Fernandes, and . Mello, A novel approach for distributed application scheduling based on prediction of communication events, Future Generation Computer Systems, 2009.

O. Dragomir, R. Gouriveau, N. Zerhouni, and F. Dragomir, FRAMEWORK FOR A DISTRIBUTED AND HYBRID PROGNOSTIC SYSTEM, 4th IFAC Conference on Management and Control of Production and Logistics ? MCPL'07, 2007.
DOI : 10.3182/20070927-4-RO-3905.00072

URL : https://hal.archives-ouvertes.fr/hal-00181342

J. L. Elman, Finding Structure in Time, Cognitive Science, vol.49, issue.2, pp.179-211, 1990.
DOI : 10.1207/s15516709cog1402_1

P. Frasconi, M. Gori, and G. Et-soda, Local Feedback Multilayered Networks, Feedback Multilayered Networks, pp.120-130, 1992.
DOI : 10.1162/neco.1989.1.2.270

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.2965

Y. Gao, M. Joo, and E. , NARMAX time series model prediction : feedforward and recurrent fuzzy neural network approaches " , Fuzzy sets and systems, pp.331-350, 2005.
DOI : 10.1016/j.fss.2004.09.015

K. Goebel and P. Bonissone, Prognostic information fusion for constant load systems, 2005 7th International Conference on Information Fusion, pp.1247-1255, 2005.
DOI : 10.1109/ICIF.2005.1592000

B. H. Gowrishankar-;-ramesh and P. Satyanarayana, Neural network based BER prediction for 802.16e channel, 2007 15th International Conference on Software, Telecommunications and Computer Networks, pp.27-29, 2007.
DOI : 10.1109/SOFTCOM.2007.4446119

G. Daniel and «. Witoldpedrycz, Fuzzy prediction architecture using recurrent neural networks, Pages 1668-1678 Advances in Machine Learning and Computational Intelligence -16th European Symposium on Artificial Neural Networks 2008, 16th European Symposium on Artificial Neural Networks, 2008.

H. Aiwina, S. Zhanga, A. C. Tana, and J. Mathewa, Rotating machinery prognostics: State of the art, challenges and opportunities, Mechanical Systems and Signal Processing, vol.23, issue.3, pp.724-739, 2009.

J. S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics, vol.23, issue.3, pp.24-38, 1993.
DOI : 10.1109/21.256541

A. K. Jardine, D. Lin, and D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, vol.20, issue.7, pp.1483-1510, 2006.
DOI : 10.1016/j.ymssp.2005.09.012

R. Joshi and C. Reeves, Beyond the Cox model: artificial neural networks for survival analysis part II, Proceedings of the Eighteenth International Conference on Systems Engineering, 2006.

K. Kazmierczak, Application of autoregressive prognostic techniques in diagnostics, Proceedings of the Vehicle Diagnostics Conference, 1983.

B. P. Leao, T. Yoneyama, G. C. Rocha, and K. T. Fitzgibbon, Prognostics performance metrics and their relation to requirements, design, verification and cost-benefit, 2008 International Conference on Prognostics and Health Management, 2008.
DOI : 10.1109/PHM.2008.4711429

J. Lee, A systematic approach for developing and deploying advanced prognostics technologies and tools: methodology and applications, Proceedings of the Second World Congress on Engineering Asset Management, pp.1195-1206, 2007.

L. Guan-chun, C. Wu-chun-yin, and . Wei-chong, Artificial immune regulation (AIR) for model-based fault diagnosis » Artificial immune systems : ( Catania, ICARIS 2004 : international conference on artificial immune systems No3, pp.13-16, 2004.

L. Ma, Condition monitoring in engineering asset management, Proceedings of Asia-Pacific Vibration Conference, 2007.

D. P. Mandic and J. A. Chambers, Recurrent Neural Networks for Prediction: Learning Algorithms and Architectures and Stability, 2001.
DOI : 10.1002/047084535X

M. Maria, P. , and G. A. Barreto, « Long-term time series prediction with the NARX network: An empirical evaluation, Pages 3335-3343 Advances in Neural Information Processing (ICONIP 2006) / Brazilian Symposium on Neural Networks, pp.16-18, 2006.

A. Muller, M. C. Suhner, and B. Iung, Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system, Reliability Engineering & System Safety, vol.93, issue.2, pp.234-253, 2008.
DOI : 10.1016/j.ress.2006.12.004

URL : https://hal.archives-ouvertes.fr/hal-00149602

N. Palluat and N. , Zerhouni « A neuro-fuzzy monitoring system: Application to flexible production systems, Computers in Industry, vol.57, issue.6, 2006.

H. T. Pham, V. T. Tran, and B. Yang, A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting, Expert Systems with Applications, vol.37, issue.4, 2009.
DOI : 10.1016/j.eswa.2009.10.020

E. Pisoni, M. Farina, C. Carnevale, and L. Piroddi, Forecasting peak air pollution levels using NARX models, Engineering Applications of Artificial Intelligence, vol.22, issue.4-5, pp.593-602, 2009.
DOI : 10.1016/j.engappai.2009.04.002

H. C. Pusey and M. J. Roemer, An assessment of turbomachinery condition monitoring and failure prognosis technology, The Shock and Vibration Digest, pp.31-365, 1999.

M. J. Roemer, G. J. Kacprzynski, E. O. Nwadiogbu, and G. Bloor, Development of diagnostic and prognostic technologies for aerospace health management applications, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542), pp.3139-3147, 2001.
DOI : 10.1109/AERO.2001.931331

B. Samantaa and S. Bandopadhyay, Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit, Computers & Geosciences, vol.35, issue.8, pp.1592-1602, 2009.
DOI : 10.1016/j.cageo.2009.01.006

A. Saxena, J. Celaya, E. Balaban, K. Goebel, B. Saha et al., Metrics for evaluating performance of prognostic techniques, 2008 International Conference on Prognostics and Health Management, 2008.
DOI : 10.1109/PHM.2008.4711436

R. Setiono, W. K. Leow, and J. Y. Thong, Opening the neural network black box: An algorithm for extracting rules from function approximating artificial neural networks, Proceedings of International Conference on Information Systems, pp.176-186, 2000.

Y. Shao and K. Nezu, Prognosis of remaining bearing life using neural networks, Proceedings of the Institution of Mechanical Engineers, pp.217-230, 2000.
DOI : 10.1243/0959651001540582

J. P. Sum, W. Kan, and Y. G. , a note on the equivalence of NARMAX anf RNN, Neural Computing & Applications, pp.8-33, 1999.

A. B. Tickle, R. Andrews, M. Golea, and J. Diederich, The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks, IEEE Transactions on Neural Networks, vol.9, issue.6, pp.1057-1068, 1998.
DOI : 10.1109/72.728352

P. Tse and D. Atherton, Prediction of Machine Deterioration Using Vibration Based Fault Trends and Recurrent Neural Networks, Journal of Vibration and Acoustics, vol.121, issue.3, pp.355-362, 1999.
DOI : 10.1115/1.2893988

G. Vachtsevanos, F. L. Lewis, M. Roemer, A. Hess, and B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, 2006.
DOI : 10.1002/9780470117842

P. Wang and G. Vachtsevanos, Fault prognostics using dynamic wavelet neural networks, AI EDAM, vol.15, issue.4, pp.349-365, 2001.
DOI : 10.1017/S0890060401154089

W. Wang, An adaptive predictor for dynamic system forecasting, Mechanical Systems and Signal Processing, vol.21, issue.2, pp.809-823, 2007.
DOI : 10.1016/j.ymssp.2005.12.008

W. Q. Wang, M. F. Golnaraghi, and F. Ismail, Prognosis of machine health condition using neuro-fuzzy systems, Mechanical Systems and Signal Processing, vol.18, issue.4, pp.813-831, 2004.
DOI : 10.1016/S0888-3270(03)00079-7

H. L. Wei, D. Q. Zhu, S. A. Billings, and M. A. , Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks, Advances in Space Research, vol.40, issue.12, pp.1863-1870, 2007.
DOI : 10.1016/j.asr.2007.02.080

R. C. Yam, P. W. Tse, L. Li, and P. Tu, Intelligent Predictive Decision Support System for Condition-Based Maintenance, The International Journal of Advanced Manufacturing Technology, vol.17, issue.5, pp.383-391, 2001.
DOI : 10.1007/s001700170173

G. Zhang, B. E. Patuwo, and M. Y. Hu, Forecasting with artificial neural networks:, International Journal of Forecasting, vol.14, issue.1, pp.35-62, 1998.
DOI : 10.1016/S0169-2070(97)00044-7

R. Zemouri, D. Racoceanu, and N. Zerhouni, ? b ? « Réseaux de neurones Récurrents à Fonction de base Radiales :RRFR/ Application au pronostic », Revue d'Intelligence Artificielle, RSTI série RIA, vol.16, p.3, 2002.

R. Zemouri, D. Racoceanu, and N. Zerhouni, Recurrent radial basis function network for time-series prediction, Engineering Applications of Artificial Intelligence, vol.16, issue.5-6, pp.453-463, 2003.
DOI : 10.1016/S0952-1976(03)00063-0

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.7475

R. Zemouri, . Paul-ciprian, and . Patic, Prediction Error Feedback for Time Series Prediction: a way to improve the accuracy of predictions », The 4th EUROPEAN COMPUTING CONFERENCE (ECC'10) WSEAS Conferences in the Universitatea Politehnica, 2010.

E. L. , S. Sp, and . Pappas, Implementation of Artificial Intelligence in the Time Series Prediction Problem, Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, 2006.

S. Kim and B. Kim, Modeling of Ion Energy Distribution Using Time-Series Neural Network, 12th WSEAS International Conference on SYSTEMS, 2008.

M. Kwon, C. Byung, B. Park, and F. Rivas-echeverría, Prediction of Chamber Leak Pattern Using Time-Series Neural Network, 12th WSEAS International Conference on SYSTEMS Time Series Forecasting using ARIMA, Neural Networks and Neo Fuzzy Neurons " , 3rd WSEAS International Conference on Neural Networks and Applications (NNA '02), Fuzzy Sets and Fuzzy Systems (FSFS '02), Evolutionary Computation (EC '02), 2002.

R. Chibanga, J. Berlamont, and J. Vandewalle, Use of Neural Networks to Forecast Time Series: River Flow Modeling, 3rd WSEAS International Conference on Neural Networks and Applications (NNA '02), Fuzzy Sets and Fuzzy Systems (FSFS '02), Evolutionary Computation (EC '02) Interlaken, 2002.

E. Balaguer-ballester, E. S. Olivas, J. L. , C. Rodriguez, and S. Del-valle-tascon, Forecasting of surface ozone concentrations 24 hours in advance using neural networks, WSES International Conferences Neural Network and Applications Fuzzy Sets and Fuzzy Systems Evolutionary Computation, 2001.

A. Lapedes and R. Farber, Nonlinear signal processing using neural networks: prediction and system modelling. The. Rep. LA-UR87-2662, 1987.