D. W. Clarke and T. Mohtadi, Properties of generalized predictive control, Automatica, vol.25, issue.6, pp.859-875, 1989.
DOI : 10.1016/0005-1098(89)90053-8

]. J. Culioli, Introduction à l'Optimisation, Ellipses, 1994.

]. C. Cutler and B. C. Ramaker, Dynamic matrix control -a computer control algorithm, Automatic Control Conference, 1980.

]. S. Filali, K. Kemih, and A. Kias, Constrained predictive control using gradient method Advances in Modelling Analysis -C, AMSE Journal, vol.57, issue.3, pp.35-46, 2002.

]. R. Fletcher, Practical Methods of Optimization, 1987.
DOI : 10.1002/9781118723203

]. W. Gesing and E. J. Davison, An Exact Penalty Function Algorithm for Solving General Constrained Parameter Optimization Problems. Automation, pp.175-188, 1979.
DOI : 10.1016/0005-1098(79)90068-2

]. G. Giannakis and E. Serpedin, A bibliography on nonlinear system identification, Signal Processing, vol.81, issue.3, pp.533-580, 2001.
DOI : 10.1016/S0165-1684(00)00231-0

]. F. Glover, Tabu Search???Part I, ORSA Journal on Computing, vol.1, issue.3, pp.190-206, 1989.
DOI : 10.1287/ijoc.1.3.190

]. F. Glover, Tabu Search???Part II, ORSA Journal on Computing, vol.2, issue.1, pp.4-32, 1990.
DOI : 10.1287/ijoc.2.1.4

]. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1989.

]. W. Greblicki, Continuous-time Hammerstein system identification, IEEE Transactions on Automatic Control, vol.45, issue.6, pp.1232-1236, 2000.
DOI : 10.1109/9.863614

]. R. Haber and H. Unbehauen, Structure identification of nonlinear dynamic systems???A survey on input/output approaches, Automatica, vol.26, issue.4, pp.651-677, 1990.
DOI : 10.1016/0005-1098(90)90044-I

M. M. Hazem and . Bayoumi, Volterra system identification using adaptive genetic algorithms, Applied Soft Computing, vol.5, pp.75-86, 2004.

]. M. Hazem and M. M. Bayoumi, An adaptive evolutionary algorithm for Volterra system identification, Pattern Recognition Letters N°, vol.26, pp.109-119, 2005.

]. D. Hernando and A. A. Desrochers, Modelling of Nonlinear Discrete-time Systems from Input-Output Data, Automatica, vol.24, issue.5, pp.629-641, 1988.

]. J. Holland, Adaptation in Natural and Artificial System, The University of, 1975.

]. I. Hunter and M. J. Korenberg, The identification of nonlinear biological systems: Wiener and Hammerstein cascade models, Biolog. Cybernet, vol.55, pp.135-144, 1986.

]. C. Kelley, Detection and Remediation of Stagnation in the Nelder--Mead Algorithm Using a Sufficient Decrease Condition, SIAM Journal on Optimization, vol.10, issue.1, pp.43-55, 1999.
DOI : 10.1137/S1052623497315203

]. E. Kenneth, J. Jer-nan, and S. Richard, Direct adaptive predictive control using gradient descent, The Journal of the Acoustical Society of America, vol.105, issue.2, p.1300, 1999.

]. H. Ki and J. Cohen, Linear and Nonlinear ARMA Model Parameter Estimation Using an Artificial Neural Network, IEEE Trans. on Biomedical Engineering, vol.44, issue.3, pp.168-174, 1997.

]. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by Simulated Annealing, Science Magazine, vol.220, pp.671-680, 1983.

]. M. Kortmann and H. Unbehauen, Structure detection in the identification of non linear systems, APII, N° 22, pp.5-25, 1988.

]. A. Kurpati, S. Azarm, and J. Wu, Constraint handling improvements for multiobjective genetic algorithms, Structural and Multidisciplinary Optimization Revue, pp.204-213, 2002.
DOI : 10.1007/s00158-002-0178-2

]. I. Leontaritis and S. A. Billings, Input-output parametric models for non-linear systems Part I: deterministic non-linear systems, International Journal of Control, vol.130, issue.2, pp.303-328, 1985.
DOI : 10.1109/TAC.1982.1103101

]. I. Leontaritis and S. A. Billings, Input-output parametric models for non-linear systems Part II: stochastic non-linear systems, International Journal of Control, vol.27, issue.2, pp.329-344, 1985.
DOI : 10.1080/0020718508961130

]. C. Li and Y. C. Jeon, Genetic Algorithm in Identifying Nonlinear Autoregressive with Exogenous Input Models for Nonlinear Systems, Proceeding of American Control Conference, pp.2305-2309, 1993.

]. C. Li and Y. C. Jeon, A Learning Controller Based on Non- Linear ARX Inverse Model Identified by a Genetic Algorithm, Proceeding of Symposium on Intelligent Process Control in ASME International Mechanical Engineering Congress and Exposition, pp.447-458, 1994.

]. W. Liu, L. Zhengpei, L. Fu, and W. Yaqi, A systematic method to identify nonlinear dynamics of BWR by using the reactor noise, Progress in Nuclear Energy, vol.43, pp.1-4, 2003.

]. M. Luersen and R. Le-riche, Globalized Nelder-Mead method for engineering optimization, Computers and Structures, pp.2251-2260, 2004.
DOI : 10.4203/ccp.76.65

]. G. Luh and G. Rizzoni, Nonlinear System Identification Using Genetic Algorithms with Application to Feedforward Control Design, Proceedings of the American Control Conference, pp.2371-2375, 1998.

]. J. Madár, J. Abonyi, and F. Szeifert, Genetic Programming for the Identification of Nonlinear Input???Output Models, Industrial & Engineering Chemistry Research, vol.44, issue.9, pp.3178-3186, 2005.
DOI : 10.1021/ie049626e

]. K. Man, K. S. Tang, and S. Kwong, Genetic algorithms: concepts and applications [in engineering design], IEEE Transactions on Industrial Electronics, vol.43, issue.5, pp.519-533, 1996.
DOI : 10.1109/41.538609

]. K. Narenda and P. Gallman, An iterative method for the identification of nonlinear systems using a Hammerstein model, IEEE Transactions on Automatic Control, issue.11, pp.546-550, 1966.

]. K. 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

]. K. Narendra and S. M. Mukhopadhyay, Adaptive control using neural networks and approximate models, IEEE Transactions on Neural Networks, vol.8, issue.3, pp.475-485, 1997.
DOI : 10.1109/72.572089

]. J. Nelder and R. Mead, A Simplex Method for Function Minimization, The Computer Journal, vol.7, issue.4, pp.308-312, 1965.
DOI : 10.1093/comjnl/7.4.308

]. C. Ong, J. J. Huang, and G. H. Tzeng, Model identification of ARIMA family using genetic algorithms, Applied Mathematics and Computation, vol.164, issue.3, pp.885-912, 2005.
DOI : 10.1016/j.amc.2004.06.044

]. E. Petlenkov, NN-ANARX structure based dynamic output feedback linearization for control of nonlinear MIMO systems, 2007 Mediterranean Conference on Control & Automation, 2007.
DOI : 10.1109/MED.2007.4433965

]. M. 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, 1965.
DOI : 10.1093/comjnl/7.2.155

]. A. Propoi, Use of linear programming methods for synthesizing sampled-data automatic systems, Automation and Remote Control, vol.24, pp.837-844, 1963.

]. T. Pukkila and P. R. Krishnaiah, On the use of autoregressive order determination criteria in univariate white noise tests, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.36, issue.5, pp.36-764, 1988.
DOI : 10.1109/29.1586

]. S. Qin and T. J. Badgwell, An overview of industrial model predictive control technology, 1997.

]. S. Rao, Engineering Optimization: Theory and Practice, 1996.
DOI : 10.1002/9780470549124

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.357.3838

]. J. Richalet, A. Rault, J. L. Testud, and J. Papon, Algorithmic control of industrial processes, 4 th IFAC Symposium on Identification and System Parameter Estimation, 1976.

]. J. Richalet, A. Rault, J. L. Testud, and J. Papon, Model predictive heuristic control, Automatica, vol.14, issue.5, pp.413-428, 1978.
DOI : 10.1016/0005-1098(78)90001-8

]. H. Rosenbrock, An Automatic Method for Finding the Greatest or Least Value of a Function, The Computer Journal, vol.3, issue.3, pp.175-184, 1960.
DOI : 10.1093/comjnl/3.3.175

]. A. Ruano, P. J. Fleming, C. Teixeira, K. Rodriguez-vazquez, and C. M. Fonseca, Nonlinear identification of aircraft gas-turbine dynamics, Neurocomputing, vol.55, issue.3-4, pp.551-579, 2003.
DOI : 10.1016/S0925-2312(03)00393-X

]. L. Sheng, K. H. Ju, and K. H. Chon, A new algorithm for linear and nonlinear ARMA model parameter estimation using affine geometry [and application to blood flow/pressure data], IEEE Transactions on Biomedical Engineering, vol.48, issue.10, pp.1116-1124, 2001.
DOI : 10.1109/10.951514

]. L. Sheng and K. H. Chon, Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance, IEEE Transactions on Signal Processing, vol.51, issue.12, pp.3020-3026, 2003.
DOI : 10.1109/TSP.2003.818999

]. F. Song and P. Li, MIMO Decoupling Control Based on Support Vector Machines ?th-order Inversion, Proceeding of the 6 th World Congress on Intelligent Control and Automation, pp.1002-1006, 2006.

]. Y. Wen and L. Xiaoou, Fuzzy identification Using Fuzzy Neural Networks with stable learning algorithms, IEEE Transactions on Fuzzy Systems, vol.12, issue.3, pp.411-420, 2004.

]. D. Westwick and M. Verhaegen, Identifying MIMO Wiener systems using subspace model identification methods, Signal Processing, vol.52, issue.2, pp.235-258, 1996.
DOI : 10.1016/0165-1684(96)00056-4

]. M. Wright, Direct Search Methods : Once Scorned, Now Respectable, Numerical Analysis, 1995.

]. M. Wright, Optimization methods for base station placement in wireless systems, Proceedings of the IEEE VTC'98, 1998.

]. X. Yang, Z. F. Yang, G. H. Lu, and J. Q. Li, A gray-encoded, hybrid-accelerated, genetic algorithm for global optimizations in dynamical systems, Communications in Nonlinear Science and Numerical Simulation, vol.10, issue.4, pp.355-363, 2005.
DOI : 10.1016/j.cnsns.2003.12.005

]. Z. Yang, T. Fujimoto, and K. Kumamaru, A Genetic Algorithm Approach to Identification of Nonlinear Polynomial Models, IFAC System Identification, 2000.

]. T. Yonghong and A. R. Van-cauwenberghe, Optimization techniques for the design of a neural predictive controller, Journal Neurocomputing, vol.10, issue.1, pp.83-96, 1996.

F. @bullet-brahim-tlili and . Bouani, Commande prédictive non linéaire'', Conférence Internationale Francophone d'Automatique (CIFA'2004), 2004.

F. @bullet-brahim-tlili, M. Bouani, and . Ksouri, Estimation des paramètres du modèle NARMA à l'aide des réseaux de neurones et des algorithmes génétiques'', Actes de la 6° conférence francophone de MOdélisation et SIMulation, 2006.

F. @bullet-brahim-tlili, M. Bouani, and . Ksouri, A derivative-free constrained predictive controller, Proceedings of the 10th WSEAS International Conference on Systems, pp.358-363, 2006.

F. @bullet-brahim-tlili, M. Bouani, and . Ksouri, Identification of multivariable NARMA models using Artificial Neural Networks'', Industrial Simulation Conference ISC, pp.40-44, 2008.

F. @bullet-brahim-tlili, M. Bouani, @. Ksouri, F. Brahim-tlili, M. Bouani et al., Constrained model predictive control using a derivative-free optimization method Identification des systèmes non linéaires par des modèles de type NARMA, WSEAS Transactions on Systems Journal Européen des Systèmes Automatisés JESA, vol.1010, issue.42, pp.2307-2313, 2006.