C. Alippi, C. De-russis, and V. Piuri, A neural-network based control solution to air-fuel ratio control for automotive fuel-injection systems, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.33, issue.2, pp.259-268, 2003.
DOI : 10.1109/TSMCC.2003.814035

P. Andersson, Intake Air Dynamics on a Turbocharged SI Engine with Wastegate, 2002.

P. Andersson and L. Eriksson, Mean-value observer for a turbocharged SI engine, Proc. of the IFAC Symp. on Advances in Automotive Control, pp.146-151, 2004.

I. Arsie, C. Pianese, and M. Sorrentino, A procedure to enhance identification of recurrent neural networks for simulating air???fuel ratio dynamics in SI engines, Engineering Applications of Artificial Intelligence, vol.19, issue.1, pp.65-77, 2006.
DOI : 10.1016/j.engappai.2005.06.003

A. R. Barron, Universal approximation bounds for superpositions of a sigmoidal function, IEEE Transactions on Information Theory, vol.39, issue.3, pp.930-945, 1993.
DOI : 10.1109/18.256500

G. Bloch and T. Denoeux, Neural networks for process control and optimization: Two industrial applications, ISA Transactions, vol.42, issue.1, pp.39-51, 2003.
DOI : 10.1016/S0019-0578(07)60112-8

G. Bloch, F. Lauer, G. Colin, and Y. Chamaillard, Combining experimental data and physical simulation models in support vector learning, Proc. of the 10th Int. Conf. on Engineering Applications of Neural Networks (EANN) CEUR Workshop Proceedings, pp.284-295, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00152249

G. Bloch, F. Sirou, V. Eustache, and P. Fatrez, Neural intelligent control for a steel plant, IEEE Transactions on Neural Networks, vol.8, issue.4, pp.910-918, 1997.
DOI : 10.1109/72.595889

URL : https://hal.archives-ouvertes.fr/hal-00992576

G. Colin, Contrôle des systèmes rapides non linéaires ? Application au moteuràteurà allumage commandé turbocompresséturbocompresséà distribution variable, 2006.

G. Colin, Y. Chamaillard, G. Bloch, and A. Charlet, Exact and Linearized Neural Predictive Control: A Turbocharged SI Engine Example, Journal of Dynamic Systems, Measurement, and Control, vol.129, issue.4, pp.527-533, 2007.
DOI : 10.1115/1.2745881

URL : https://hal.archives-ouvertes.fr/hal-00151807

G. Colin, Y. Chamaillard, G. Bloch, and G. Corde, Neural Control of Fast Nonlinear Systems— Application to a Turbocharged SI Engine With VCT, IEEE Transactions on Neural Networks, vol.18, issue.4, pp.1101-1114, 2007.
DOI : 10.1109/TNN.2007.899221

G. Corde, Le contrôle moteur, Contrôle commande de la voiture. Hermès, 2002.

J. Daafouz and J. Bernussou, Parameter dependent Lyapunov functions for discrete time systems with time varying parametric uncertainties, Systems & Control Letters, vol.43, issue.5, pp.355-359, 2001.
DOI : 10.1016/S0167-6911(01)00118-9

G. De-nicolao, R. Scattolini, and C. Siviero, Modelling the volumetric efficiency of ic engines: Parametric, non-parametric and neural techniques, Control Engineering Practice, vol.4, issue.10, pp.1405-1415, 1996.
DOI : 10.1016/0967-0661(96)00150-5

P. M. Drezet and R. F. Harrison, Support vector machines for system identification, UKACC International Conference on Control (CONTROL '98), pp.688-692, 1998.
DOI : 10.1049/cp:19980312

J. W. Fox, W. K. Cheng, and J. B. Heywood, A Model for Predicting Residual Gas Fraction in Spark-Ignition Engines, SAE Technical Paper Series, 1993.
DOI : 10.4271/931025

J. Gerhardt, H. Hönniger, and H. Bischof, A new approach to functionnal and software structure for engine management systems -BOSCH ME7, SAE Technical Papers, issue.980801, 1998.

P. Giansetti, G. Colin, P. Higelin, and Y. Chamaillard, Residual gas fraction measurement and computation, International Journal of Engine Research, vol.8, issue.9, pp.347-364, 2007.
DOI : 10.1243/14680874JER00407

L. Guzzella and C. H. Onder, Introduction to Modeling and control of Internal Combustion Engine Systems, 2004.

E. Hendricks and J. Luther, Model and observer based control of internal combustion engines, Proc. of the 1st Int. Workshop on Modeling Emissions and Control in Automotive Engines (MECA), pp.9-20, 2001.

. Imagine, Amesim web site. www.amesim.com, 2006.

M. Jankovic and S. W. Magner, VARIABLE CAM TIMING: CONSEQUENCES TO AUTOMOTIVE ENGINE CONTROL DESIGN, Proc. of the 15th Triennial IFAC World Congress, 2002.
DOI : 10.3182/20020721-6-ES-1901.01513

I. Kolmanovsky, Support vector machine-based determination of gasoline directinjected engine admissible operating envelope, SAE Technical Papers, 1301.

M. Lairi and G. Bloch, A neural network with minimal structure for maglev system modeling and control, Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014), pp.40-45, 1999.
DOI : 10.1109/ISIC.1999.796627

URL : https://hal.archives-ouvertes.fr/hal-00405124

F. Lauer and G. Bloch, Incorporating prior knowledge in support vector regression, Machine Learning, 2007.
DOI : 10.1007/s10994-007-5035-5

URL : https://hal.archives-ouvertes.fr/hal-00178619

F. , L. Berr, M. Miche, G. Colin, G. L. Solliec et al., Modelling of a Turbocharged SI Engine with Variable Camshaft Timing for Engine Control Purposes, SAE Technical Paper, 2006.

B. Lecointe and G. Monnier, Downsizing a Gasoline Engine Using Turbocharging with Direct Injection, SAE Technical Paper Series, 2003.
DOI : 10.4271/2003-01-0542

L. Ljung, System identification: Theory for the user, 1999.

O. Mangasarian, Generalized support vector machines, Advances in Large Margin Classifiers, pp.135-146, 2000.

O. L. Mangasarian and D. R. Musicant, Large scale kernel regression via linear programming, Machine Learning, pp.255-269, 2002.

K. A. Marko, Neural network application to diagnostics and control of vehicle control systems, Advances in Neural Information Processing Systems, pp.537-543

D. Mattera and S. Haykin, Support vector machines for dynamic reconstruction of a chaotic system, Advances in kernel methods: support vector learning, pp.211-241, 1999.

G. Millérioux, F. Anstett, and G. Bloch, Considering the attractor structure of chaotic maps for observer-based synchronization problems, Mathematics and Computers in Simulation, vol.68, issue.1, pp.67-85, 2005.
DOI : 10.1016/j.matcom.2004.10.001

G. Millérioux, L. Rosier, G. Bloch, and J. Daafouz, Bounded State Reconstruction Error for LPV Systems With Estimated Parameters, IEEE Transactions on Automatic Control, vol.49, issue.8, pp.1385-1389, 2004.
DOI : 10.1109/TAC.2004.832669

O. Nelles, Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models, 2001.

M. J. Orr, Recent advances in radial basis function networks, 1999.

T. Poggio and F. Girosi, Networks for approximation and learning, Proc. IEEE, pp.1481-1497, 1990.
DOI : 10.1109/5.58326

G. V. Puskorius and L. A. Feldkamp, Parameter-Based Kalman Filter Training: Theory and Implementation, Kalman filtering and neural networks, chapter 2, pp.23-67, 2001.
DOI : 10.1002/0471221546.ch2

A. Rakotomamonjy, R. Le-riche, D. Gualandris, and Z. Harchaoui, A comparison of statistical learning approaches for engine torque estimation, Control Engineering Practice, vol.16, issue.1, 2007.
DOI : 10.1016/j.conengprac.2007.03.009

URL : https://hal.archives-ouvertes.fr/hal-00439467

R. Reed, Pruning algorithms-a survey, IEEE Transactions on Neural Networks, vol.4, issue.5, pp.740-747, 1993.
DOI : 10.1109/72.248452

M. Rychetsky, S. Ortmann, and M. Glesner, Support vector approaches for engine knock detection, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), pp.969-974, 1999.
DOI : 10.1109/IJCNN.1999.831085

B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2001.

P. J. Shayler, M. S. Goodman, and T. Ma, The exploitation of neural networks in automotive engine management systems, Engineering Applications of Artificial Intelligence, vol.13, issue.2, pp.147-157, 2000.
DOI : 10.1016/S0952-1976(99)00048-2

J. Sjöberg and L. S. Ngia, Neural Nets and Related Model Structures for Nonlinear System Identification, Nonlinear Modeling, Advanced Black-Box Techniques, chapter 1, pp.1-28, 1998.
DOI : 10.1007/978-1-4615-5703-6_1

J. Sjöberg, Q. Zhang, L. Ljung, A. Benveniste, B. Delyon et al., Nonlinear black-box modeling in system identification: a unified overview, Automatica, vol.31, issue.12, pp.311691-1724, 1995.
DOI : 10.1016/0005-1098(95)00120-8

A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Statistics and Computing, vol.14, issue.3, pp.199-222, 2004.
DOI : 10.1023/B:STCO.0000035301.49549.88

A. J. Smola, B. Schölkopf, and G. Rätsch, Linear programs for automatic accuracy control in regression, 9th International Conference on Artificial Neural Networks: ICANN '99, pp.575-580, 1999.
DOI : 10.1049/cp:19991171

A. Stotsky and I. Kolmanovsky, Application of input estimation techniques to charge estimation and control in automotive engines, Control Engineering Practice, vol.10, issue.12, pp.1371-1383, 2002.
DOI : 10.1016/S0967-0661(02)00101-6

P. Thomas and G. Bloch, Robust pruning for multilayer perceptrons, Proc. of the IMACS/IEEE Multiconf. on Computational Engineering in Systems Applications, pp.17-22, 1998.

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

C. Vong, P. Wong, and Y. Li, Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference, Engineering Applications of Artificial Intelligence, vol.19, issue.3, pp.277-287, 2006.
DOI : 10.1016/j.engappai.2005.09.001

L. Zhang and Y. Xi, Nonlinear System Identification Based on an Improved Support Vector Regression Estimator, Proc. of the Int. Symp. on Neural Networks, pp.586-591, 2004.
DOI : 10.1007/978-3-540-28647-9_96