M. Schetzen, The Voltera and Wiener Theories of Nonlinear Systems, 1980.

N. Wiener, Non linear problems in random theory, 1958.

C. H. Lu and C. Tsai, Generalized predictive control using recurrent fuzzy neural networks for industrial processes, Journal of Process Control, vol.17, issue.1, pp.83-92, 2007.
DOI : 10.1016/j.jprocont.2006.08.003

S. Srivastava, M. Sing, M. Hanmandlu, and A. N. Jha, New fuzzy wavelet neural networks for system identification and control, Applied Soft Computing, vol.6, issue.1, pp.1-17, 2005.
DOI : 10.1016/j.asoc.2004.10.001

K. Patan and T. Parisini, Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process, Journal of Process Control, vol.15, issue.1, pp.67-79, 2005.
DOI : 10.1016/j.jprocont.2004.04.001

H. R. Maier and G. C. Dandy, The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters, Water Resources Research, vol.13, issue.3, pp.1013-1022, 1996.
DOI : 10.1029/96WR03529

K. Gasso, Identification de systèmes dynamiques non linéaires : approche multi modèle, 2000.

K. Madani and L. Thiaw, Self-organizing multi-modeling: A different way to design intelligent predictors, Neurocomputing, vol.70, issue.16-18, pp.2836-2852, 2007.
DOI : 10.1016/j.neucom.2006.06.016

H. Vernieuwe, O. Georgieva, B. D. Baets, V. R. Pauwels, N. E. Verhoestand et al., Comparison of data-driven Takagi???Sugeno models of rainfall???discharge dynamics, Journal of Hydrology, vol.302, issue.1-4, pp.1-14, 2004.
DOI : 10.1016/j.jhydrol.2004.07.001

L. Thiaw, M. Rybnik, R. Malti, A. Chebira, and K. Madani, A comparative study between a multi-models based approach and an artificial neural network based

G. Dreyfus, Réseaux de Neurones -Méthodologie et applications, Eyrolles, 2002.

B. Irie and S. Miyake, Capabilities of tree-layered perceptrons, Proceedings of the IEEE Second International Conference on Neural Networks, pp.641-647, 1988.

G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals, and Systems, vol.27, issue.4, pp.303-314, 1989.
DOI : 10.1007/BF02551274

H. Akaike, Fitting autoregressive models for prediction, Annals of the Institute of Statistical Mathematics, vol.28, issue.1, pp.243-247, 1969.
DOI : 10.1007/BF02532251

T. A. Johansen and B. A. Foss, Nonlinear Local Model Representation For Adaptive Systems, Singapore International Conference on Intelligent Control and Instrumentation [Proceedings 1992], pp.677-682, 1992.
DOI : 10.1109/SICICI.1992.637617

P. J. Werbos, Backpropagation through time: what it does and how to do it, Proceedings of the IEEE, pp.1550-1560, 1990.
DOI : 10.1109/5.58337

R. J. Williams and D. Zipser, A Learning Algorithm for Continually Running Fully Recurrent Neural Networks, Neural Computation, vol.11, issue.2, pp.270-280, 1989.
DOI : 10.1016/0885-064X(88)90021-0

K. Madani and L. Thiaw, Multi-model based identification : Application to nonlinear dynamic behavior prediction, International Multi-Conference on Advanced Computer System and Computer Information Systems and Industrial Management, 2005.

H. Vernieuve, B. De-baets, and N. E. Verhoest, Comparison of clustering algorithms in the identification of takagi-sugeno models : A hydrological case study. Fuzzy Sets and Systems, pp.2876-2896, 2006.

R. Babuska, Fuzzy modelling for control, 1998.

L. Thiaw, K. Madani, R. Malti, and G. Sow, Implementation of recurrent multimodels for system identification, International Conference on Informatics in Control, Automation and Robotics (ICINCO), 2007.
URL : https://hal.archives-ouvertes.fr/hal-00182394

L. Thiaw, K. Madani, R. Malti, and G. Sow, NARMAX multi-model based dynamic nonlinear systems behaviour prediction, Workshop on Advanced Control and Diagnosis (IAR-ACD), 2005.

G. Box and G. Jenkins, Time series analysis, forecasting and control, pp.532-533, 1970.

L. Ljuing, System Identification : Theory for the User, 1987.

D. Urbani, Méthodes statistiques de sélection d'architectures neuronales : applicationàtion`tionà la conception de modèles de processus dynamiques, 1995.

T. Cibas, F. F. Soulié, P. Gallinari, and S. Raudys, Variable selection with neural networks, Neurocomputing, vol.12, issue.2-3, pp.223-248, 1996.
DOI : 10.1016/0925-2312(95)00121-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.357

S. Wu and M. J. Er, Dynamic fuzzy neural networks -a novel approach to function approximation, IEEE Transactions on systems, man, and cybernetics -part B, vol.30, issue.2, pp.358-364, 2000.

N. Li, S. Y. Li, and Y. G. Xi, Multi-model predictive control based on the Takagi???Sugeno fuzzy models: a case study, Information Sciences, vol.165, issue.3-4, pp.247-263, 2004.
DOI : 10.1016/j.ins.2003.10.011

L. X. Wang, A course in fuzzy systems and control, 1997.

T. Takagi and M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, vol.15, issue.1, pp.116-132, 1985.
DOI : 10.1109/TSMC.1985.6313399

M. J. Powell, Radial basis functions for multivariable interpolation : A review

N. B. Karayiannis and S. Behnkec, New radial basis neural networks and their application in a large-scale handwritten digit recognition problem, 2000.

J. Park and I. W. Sandberg, Approximation and Radial-Basis-Function Networks, Neural Computation, vol.2, issue.2, pp.305-316, 1993.
DOI : 10.1162/neco.1991.3.2.246

S. Dablemont, G. Simon, A. Lendasse, A. Ruttiens, F. Blayo et al., Time series forecasting with SOM and local-non-linear models -Application to the DAX30 index prediction, Workshop on Self-Organizing Maps, pp.340-345, 2003.

A. Lendasse, M. Cottrell, V. Wertz, and M. Verleysen, Prediction of electric load using Kohonen maps - Application to the Polish electricity consumption, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), 2002.
DOI : 10.1109/ACC.2002.1024500

A. Bezerianos, S. Papadimitriou, and D. , Radial basis function neural networks for the characterization of heart rate variability dynamics, Artificial Intelligence in Medicine, vol.15, issue.3, pp.215-234, 1999.
DOI : 10.1016/S0933-3657(98)00055-4

V. N. Vapnik, The nature of statistical learning theory, 1995.

J. Shena, Y. R. Syaub, and E. S. Lee, Support vector fuzzy adaptive network in regression analysis, Computers & Mathematics with Applications, vol.54, issue.11-12, pp.1353-1366, 2007.
DOI : 10.1016/j.camwa.2007.03.006

A. B. Gandhia, J. B. Joshia, V. K. Jayaramanb, and B. D. Kulkarni, Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas???liquid systems, Chemical Engineering Science, vol.62, issue.24, pp.7078-7089, 2007.
DOI : 10.1016/j.ces.2007.07.071

B. R. Chang and H. F. Tsai, Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity, Expert Systems with Applications, vol.34, issue.2, pp.925-934, 2008.
DOI : 10.1016/j.eswa.2006.10.034

Y. Gao and M. J. Er, Narmax time series model prediction : feedforward and recurrent fuzzy neural network approaches. Fuzzy Sets and Sytems, pp.331-350, 2005.
DOI : 10.1016/j.fss.2004.09.015

W. Z. Yang, Y. L. Cheng, and L. , Narmax model representation and its application to damage detection for multi-laer composites, Composite Structures, vol.68, pp.109-117, 2005.

H. K. Fung-eric, Y. K. Wong, H. F. Ho, and M. P. Mignolet, Modelling and prediction of machining errors using ARMAX and NARMAX structures, Applied Mathematical Modelling, vol.27, issue.8, pp.611-627, 2003.
DOI : 10.1016/S0307-904X(03)00071-4

M. Stone, An asymptotic equivalence of choice of model by cross-validation and akaike's criterion, J. R. Stat. Soc, vol.38, pp.44-47, 1977.

L. Breiman, Heuristics of instability and stabilization in model selection, The Annals of Statistics, vol.24, issue.6, pp.2350-2383, 1996.
DOI : 10.1214/aos/1032181158

B. Efron and R. J. Tibshirani, Improvements on cross-validation : The .632+ bootstrap method, J. of the American Statistical Association, vol.92, pp.548-560, 1997.

H. Akaike, A new look at the statistical model identification, IEEE Trans on Automatic control, vol.9, pp.716-723, 1974.

J. Rissanen, Modeling by shortest data description, Automatica, vol.14, issue.5, pp.465-471, 1978.
DOI : 10.1016/0005-1098(78)90005-5

M. Bekara, Optimisation de critères de choix de modèles pour un faible nombre de données, 2004.

J. C. Carmona, ContributionàContributionà l'identification et la commande robuste de systèmes dynamiques complexes : de la théoriè a l'application en mécanique. HDR, Septembre, 2004.

T. A. Johansen, Robust identification of Takagi-Sugeno-Kang fuzzy models using regularization, Proceedings of IEEE 5th International Fuzzy Systems, 1996.
DOI : 10.1109/FUZZY.1996.551739

T. A. Johansen, On Tikhonov regularization, bias and variance in nonlinear system identification, Automatica, vol.33, issue.3, pp.441-446, 1997.
DOI : 10.1016/S0005-1098(96)00168-9

J. C. Nash, Compact numerical Methods for Computers, linear algebra and function minimisation, 1990.

M. J. Powell, An efficient method for finding the minimum of a function of several variables without calculating derivatives, The Computer Journal, vol.7, issue.2, pp.155-162, 1964.
DOI : 10.1093/comjnl/7.2.155

M. N. Eskander, Neural network controller for a permanent magnet generator applied in a wind energy conversion system, Renewable Energy, vol.26, issue.3, pp.463-477, 2002.
DOI : 10.1016/S0960-1481(01)00140-9

A. A. Vartak, M. Georgiopoulos, and G. C. Anagnostopoulos, On-line Gauss???Newton-based learning for fully recurrent neural networks, Nonlinear Analysis: Theory, Methods & Applications, vol.63, issue.5-7, pp.867-876, 2005.
DOI : 10.1016/j.na.2005.02.015

K. S. Narendra and K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEEE Transactions on Neural Networks, vol.1, issue.1, pp.4-27, 1990.
DOI : 10.1109/72.80202

M. Hüsken and P. Stagge, Recurrent neural networks for time series classification, Neurocomputing, vol.50, pp.223-235, 2003.
DOI : 10.1016/S0925-2312(01)00706-8

T. Lee, Back-propagation neural network for the prediction of the short-term storm surge in Taichung harbor, Taiwan, Engineering Applications of Artificial Intelligence, vol.21, issue.1, pp.63-72, 2008.
DOI : 10.1016/j.engappai.2007.03.002

T. G. Barbounis and J. B. Theocharis, Locally recurrent neural networks for long-term wind speed and power prediction, Neurocomputing, vol.69, issue.4-6, pp.466-496, 2006.
DOI : 10.1016/j.neucom.2005.02.003

U. Konur and A. Okatan, Time series prediction using recurrent neural network architectures and time delay neural networks, ENFORMATIKA, pp.1305-1313, 2004.

Q. P. Hu, M. Xie, S. H. Ng, and G. Levitin, Robust recurrent neural network modeling for software fault detection and correction prediction, Reliabilituy engineering & System Safety, pp.332-340, 2007.
DOI : 10.1016/j.ress.2006.04.007

I. Güler and E. D. Ubeyli, A mixture of experts network structure for modelling Doppler ultrasound blood flow signals, Computers in Biology and Medicine, vol.35, issue.7, pp.565-582, 2005.
DOI : 10.1016/j.compbiomed.2004.04.001

G. Carpenter and S. Grossberg, Neural dynamics of category learning and recognition : vigilance, memory consolidation and amnesia, AAAS Symposium series), 1986.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, vol.85, issue.6088, pp.533-536, 1986.
DOI : 10.1038/323533a0

P. D. Mandic and J. A. Chambers, Recurrent neural networks for prediction -Learning algorithms, architectures and stability, 2001.

S. E. Fahlman and C. Lebiere, The cascade-correlation learning architecture, Advances in Neural Information Processing Systems, 1989.

M. Frean, The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks, Neural Computation, vol.2, issue.2, pp.198-209, 1990.
DOI : 10.1088/0305-4470/22/12/019

A. S. Weigend and D. E. Rumelhart, The e?ective dimension of the space of hidden units, International Joint Conference on Neural Networks, pp.2069-2074, 1991.

Y. L. Cun, J. Denker, and S. Solla, Optimal brain damage, Advances in Neural Information Processing Systems, pp.598-605, 1990.

L. Ozkan and M. V. Kothare, Stability analysis of a multi-model predictive control algorithm with application to control of chemical reactors, Journal of Process Control, vol.16, issue.2, pp.81-90, 2006.
DOI : 10.1016/j.jprocont.2005.06.013

D. Palma and L. Magni, A multi-model structure for model predictive control, Annual Reviews in Control, vol.28, issue.1, pp.47-52, 2004.
DOI : 10.1016/j.arcontrol.2004.01.004

R. Murray-smith and T. A. Johansen, Multiple model approach to modelling and control, 1997.

T. A. Johansen and A. B. Foss, Identification of non-linear system structure and parameters using regime decomposition, Automatica, vol.31, issue.2, pp.321-326, 1995.
DOI : 10.1016/0005-1098(94)00096-2

M. Sugeno and G. T. Kang, Structure identification of fuzzy model. Fuzzy sets and systems, pp.15-33, 1988.

C. T. Sun, Rule-base structure identification in an adaptive-network-based fuzzy inference system, IEEE Trans. on Fuzzy Systems, vol.2, issue.1, pp.64-73, 1994.

R. Murray-smith, A local model network approach to nonlinear modelling, 1994.

S. Ernst, Hinging hyperplane trees for approximation and identification, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), 1998.
DOI : 10.1109/CDC.1998.758452

R. Babuska and H. B. Verbruggen, Fuzzy sets methods for local modelling and control, chapter 2, 1997.

R. Babuska and H. B. Verbruggen, Identification of composite linear models via fuzzy clustering, Proc. of ECC, Italy, pp.1207-1212, 1995.

R. Shorten and R. Murray-smith, Multiple model approches to modelling and control, chapter Side-e?ects of normalizing basis functions in local model networks, 1997.

R. Babuska, P. J. Van-der-veen, and U. Kaymak, Improved covariance estimation for gustafson-kessel clustering, Proceedings of FUZZ-IEEE, pp.1081-1085, 2002.

J. Bezdek, Pattern recognition with fuzzy objective function algorithms, 1981.
DOI : 10.1007/978-1-4757-0450-1

D. Gustafson and W. Kessel, Fuzzy clustering with a fuzzy covariance matrix, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, pp.761-766, 1979.
DOI : 10.1109/CDC.1978.268028

S. L. Chiu, Fuzzy model identification based on cluster estimation, Intell. Fuzzy Sets, vol.2, pp.267-278, 1994.

K. Demirli, S. Cheng, and P. Muthukumaran, Subtractive clustering based modeling of job sequencing with parametric search. Fuzzy Sets and Systems, pp.235-270, 2003.

A. Subasi, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Systems with Applications, vol.32, issue.4, pp.1084-1093, 2007.
DOI : 10.1016/j.eswa.2006.02.005

S. Li, Q. Yin, P. Guo, and M. R. Lyu, A hierarchical mixture model for software reliability prediction, Applied Mathematics and Computation, vol.185, issue.2, 2006.
DOI : 10.1016/j.amc.2006.07.028

R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. Hinton, Adaptive Mixtures of Local Experts, Neural Computation, vol.4, issue.1, pp.79-87, 1991.
DOI : 10.1162/neco.1989.1.2.281

M. I. Jordan and R. A. Jacobs, Hierarchical Mixtures of Experts and the EM Algorithm, Neural Computation, vol.26, issue.2, pp.181-214, 1994.
DOI : 10.1214/aos/1176346060

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of theRoyal Statistical Society : Series B, vol.39, issue.1, pp.1-38, 1977.

M. Welling, The expectation maximization (EM) algorithm. California Institute of Technology 136-93 Pasadena

S. Borman, The expectation maximization algorithm a short tutorial. emtut@seanborman .com, 2004.

M. Farinas and C. E. Pedreira, Mixture of experts and local-global neural networks, Poceedings -European Symposium on Artificial Neural Networks (ESANN), pp.331-336, 2003.

M. Zounemat-kermani and M. Teshnehlab, Using adaptive neuro-fuzzy inference system for hydrological time series prediction, Applied Soft Computing, vol.8, issue.2, pp.928-936, 2008.
DOI : 10.1016/j.asoc.2007.07.011

R. Orjuela, D. Maquin, and J. Ragot, Identification des systèmes non linéaires par une approche multi-modèlè a ´ etats découplés, Journées Identification et Modélisation Expérimentale JIME'2006 -16 et 17 novembre -Poitiers, 2006.

T. A. Clair and J. M. Ehrman, Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers, Water Resources Research, vol.33, issue.3, pp.447-455, 1998.
DOI : 10.1029/97WR03472

S. Sakakima, T. Kojiri, and K. Itoh, Real-time reservoir operation with neural nets concept, Applications of artificial intelligence in engineering VII. Computational Mechanics Publicationns, pp.502-514, 1992.

N. Flipo, S. Even, M. Poulin, and S. Théry, Modélisation des transfert d'azote sur le bassin du Grand Morin avec la plate-forme de modélisation cawaqs, p.77305

W. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, issue.5, pp.115-133, 1943.

D. 0. Hebb, The Organization of Behavior : A neuropsychological Theory, 1949.

F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, pp.386-408, 1958.
DOI : 10.1037/h0042519

J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proceedings of National Academy of Sciences, pp.2554-2558, 1982.

D. J. Burr, Experiments on neural net recognition of spoken and written text, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.36, issue.7, pp.1162-1168, 1988.
DOI : 10.1109/29.1643

R. P. Gorman and T. J. Sejnowski, Learned classification of sonar targets using a massively parallel network, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.36, issue.7, pp.1135-1140, 1988.
DOI : 10.1109/29.1640

T. J. Sejnowski and C. R. Rosenberg, Parallel networks that learn to pronounce english text, Complex Syst, vol.1, pp.145-168, 1987.

M. Bielikova, Recurrent neural network training with the extended kalman filter, IIT. SRC, pp.57-64, 2005.

Y. Cheng, T. W. Karjala, and D. M. Himmelblau, Closed Loop Nonlinear Process Identification Using Internally Recurrent Nets, Neural Networks, vol.10, issue.3, pp.573-586, 1997.
DOI : 10.1016/S0893-6080(96)00097-4

A. Savran, An adaptive recurrent fuzzy system for nonlinear identification, Applied Soft Computing, vol.7, issue.2, 2006.
DOI : 10.1016/j.asoc.2006.02.010

K. Madani, L. Thiaw, and R. Malti, Sow : Multi-modeling : a different way to design intelligent predictors, Lecture Notes in Computer Science

K. Madani and L. Thiaw, Multi-model based identification : Application to nonlinear dynamic behavior prediction, Image Analysis, Computer Graphics, Security Systems and Artificial intelligence Applications, pp.365-375, 2005.

K. Madani and L. Thiaw, Self-organizing multi-modeling: A different way to design intelligent predictors, Neurocomputing, vol.70, issue.16-18, pp.2836-2852, 2007.
DOI : 10.1016/j.neucom.2006.06.016

L. Thiaw and K. Madani, Self-organizing multi-model based identification : Application to nonlinear dynamic systems' behavior prediction, Image Processing & Communication Journal, vol.10, pp.63-74, 2006.

L. Thiaw, M. Rybnik, R. Malti, A. Chebira, and K. Madani, A comparative study between a multi-models based approach and an artificial neural network based technique for nonlinear systems identification, International Scientific Journal of Computing, vol.3, issue.1, pp.66-74, 2004.