E. Goldratt and J. Cox, The goal: A process of ongoing improvement, 1992.

A. Thomas and P. Charpentier, Reducing simulation models for scheduling manufacturing facilities, European Journal of Operational Research, vol.161, issue.1, pp.111-125, 2005.
DOI : 10.1016/j.ejor.2003.08.042

E. H. Page, D. M. Nicol, O. Balci, R. M. Fujimoto, P. A. Fishwick et al., An aggregate production planning framework for the evaluation of volume flexibility, Proc. of the 1999 Winter Simulation Conference, pp.1509-1520, 1999.

S. C. Ward, Arguments for Constructively Simple Models, Journal of the Operational Research Society, vol.40, issue.2, pp.141-153, 1989.
DOI : 10.1057/jors.1989.19

R. J. Brooks and A. M. Tobias, Simplification in the simulation of manufacturing systems, International Journal of Production Research, vol.38, issue.5, pp.1009-1027, 2000.
DOI : 10.1080/002075400188997

L. Chwif, R. J. Paul, M. R. Pereira, and . Barretto, Discrete event simulation model reduction: A causal approach, Simulation Modelling Practice and Theory, vol.14, issue.7, pp.930-944, 2006.
DOI : 10.1016/j.simpat.2006.05.001

P. Thomas and A. Thomas, Expérimentation de la reduction d'un modèle de simulation par réseau de neurones: cas d'une scierie, 7 ème Conf. Int. de MOdélisation et de SIMulation MOSIM'08, 2008.

P. Thomas, G. Bloch, F. Sirou, and V. Eustache, Neural modeling of an induction furnace using robust learning criteria, Journal of Integrated Computer Aided Engineering, vol.6, issue.1, pp.5-23, 1999.

P. Thomas, D. Choffel, and A. Thomas, Simulation Reduction Models Approach Using Neural Network, Tenth International Conference on Computer Modeling and Simulation (uksim 2008), 2008.
DOI : 10.1109/UKSIM.2008.73

URL : https://hal.archives-ouvertes.fr/hal-00282804

P. L. Bartlett, For valid generalization, the size of the weights is more important than the size of the network, Neural Information Processing Systems, pp.134-140, 1997.

G. C. Cawley and N. L. Talbot, Preventing over-fitting during model selection via Bayesian regularisation of the hyper-parameters, Journal of Machine Learning Research, vol.8, pp.841-861, 2007.

H. Drucker, Effect of pruning and early stopping on performance of a boosting ensemble, Computational Statistics & Data Analysis, vol.38, issue.4, pp.393-406, 2002.
DOI : 10.1016/S0167-9473(01)00067-6

C. M. Bishop, Neural network for pattern recognition, 1995.

P. Lauret, E. Fock, T. A. Mara, C. Chentouf, and . Jutten, A node pruning algorithm based on a Fourier amplitude sensitivity test method Combining sigmoids and radial basis function in evolutive neural architecture, European Symp. on Artificial Neural Network ESANN'96, pp.273-293, 1996.

R. Setiono, Feedforward Neural Network Construction Using Cross Validation, Neural Computation, vol.13, issue.12, pp.2865-2877, 2001.
DOI : 10.1109/72.728361

I. Rivals and L. Personnaz, Neural-network construction and selection in nonlinear modeling, IEEE Transactions on Neural Networks, vol.14, issue.4, pp.804-819, 2003.
DOI : 10.1109/TNN.2003.811356

URL : https://hal.archives-ouvertes.fr/hal-00797670

L. Ma and K. Khorasani, New training strategies for constructive neural networks with application to regression problems, Neural Networks, vol.17, issue.4, pp.589-609, 2004.
DOI : 10.1016/j.neunet.2004.02.002

B. Hassibi and D. G. Stork, Second order derivatives for network pruning: optimal brain surgeon, Advances in Neural Information Processing Systems, pp.164-171, 1993.

M. Cottrell, B. Girard, Y. Girard, M. Mangeas, and C. Muller, Neural modeling for time series: A statistical stepwise method for weight elimination, IEEE Transactions on Neural Networks, vol.6, issue.6, pp.1355-1264, 1995.
DOI : 10.1109/72.471372

P. Thomas and G. Bloch, Robust pruning for multilayer perceptrons, IMACS/IEEE Multiconference on Computational Engineering in Systems Applications CESA'98, pp.17-22, 1998.

J. Xu and D. W. Ho, A new training and pruning algorithm based on node dependence and Jacobian rank deficiency, Neurocomputing, vol.70, issue.1-3, pp.544-558, 2006.
DOI : 10.1016/j.neucom.2005.11.005

X. Zeng and D. S. Yeung, Hidden neuron pruning of multilayer perceptrons using a quantified sensitivity measure, Neurocomputing, vol.69, issue.7-9, pp.825-837, 2006.
DOI : 10.1016/j.neucom.2005.04.010

X. Liang, Removal of hidden neurons in MLP by orthogonal projection and weight crosswise propagation, Neural Computing and Applications, pp.57-68, 2007.

E. Romero and J. M. Sopena, Performing Feature Selection With Multilayer Perceptrons, IEEE Transactions on Neural Networks, vol.19, issue.3, pp.431-441, 2008.
DOI : 10.1109/TNN.2007.909535

I. T. Jollife, Principal component analysis, 1986.
DOI : 10.1007/978-1-4757-1904-8

P. Demartines, Analyse de données par réseaux de neurones autoorganisés, 1995.

H. Stoppiglia, G. Dreyfus, R. Dubois, and Y. Oussar, Ranking a random feature for variable and feature selection, Journal of Machine Learning Research, vol.3, pp.1399-1414, 2003.

T. Cibas, F. Fogelman-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

P. Leray and P. Gallinari, FEATURE SELECTION WITH NEURAL NETWORKS, Behaviormetrika, vol.26, issue.1, pp.145-166, 1999.
DOI : 10.2333/bhmk.26.145

G. Castellano and A. M. Fanelli, Variable selection using neural-network models, Neurocomputing, vol.31, issue.1-4, pp.1-13, 2000.
DOI : 10.1016/S0925-2312(99)00146-0

S. Gadat and L. Younes, A stochastic algorithm for feature selection in pattern recognition, Journal of Machine Learning Research, vol.8, pp.509-547, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00714862

R. Setiono and W. K. Leow, Pruned Neural Networks for Regression, th Pacific RIM Int. Conf. on Artificial Intelligence PRICAI'00, pp.500-509, 2000.
DOI : 10.1007/3-540-44533-1_51

A. P. Engelbrecht, A new pruning heuristic based on variance analysis of sensitivity information, IEEE Transactions on Neural Networks, vol.12, issue.6, pp.1386-1399, 2001.
DOI : 10.1109/72.963775

B. P. Zeigler, Theory of modeling and simulation, 1976.

G. S. Innis and E. Rexstad, Simulation model simplification techniques, SIMULATION, vol.41, issue.1, pp.7-15, 1983.
DOI : 10.1177/003754978304100101

R. C. Leachman, Preliminary design and development of a corporate level production planning system for the semi conductor industry, Eds Optimization in industry, 1986.

Y. F. Hung and R. C. Leachman, Reduced simulation models of wafer fabrication facilities, International Journal of Production Research, vol.37, issue.12, pp.2685-2701, 1999.
DOI : 10.1080/002075499190473

J. S. Hwang, S. Hsieh, and H. C. Chou, A Petri net based structure for AS/RS operation modeling, International Journal Production Research, vol.36, pp.3323-3346, 1999.

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

K. Funahashi, On the approximate realization of continuous mappings by neural networks, Neural Networks, vol.2, issue.3, pp.183-192, 1989.
DOI : 10.1016/0893-6080(89)90003-8

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

C. Jutten and O. Fambon, Pruning methods: a review, Proc. of European Symp. on Artificial Neural Network ESANN'95, pp.129-140, 1995.

Y. Lecun, J. S. Denker, and S. A. Solla, Optimal brain damage, Adv. Neural Inf. Process. Syst, vol.2, pp.598-605, 1990.

M. Norgaard, System identification and control with neural networks, 1996.

C. S. Leung, K. W. Wong, P. F. Sum, and L. W. Chan, A pruning method for the recursive least squared algorithm, Neural Networks, vol.14, issue.2, pp.147-174, 2001.
DOI : 10.1016/S0893-6080(00)00093-9

H. S. Tang, S. T. Xue, R. Chen, and T. Sato, H??? Filtering in Neural Network Training and Pruning with Application to System Identification, Journal of Computing in Civil Engineering, vol.21, issue.1, pp.47-58, 2007.
DOI : 10.1061/(ASCE)0887-3801(2007)21:1(47)

M. E. Ricotti and E. Zio, Neural network approach to sensitivity and uncertainty analysis, Reliability Engineering & System Safety, vol.64, issue.1, pp.59-71, 1999.
DOI : 10.1016/S0951-8320(98)00057-X

D. Sabo and X. H. Yu, A new pruning algorithm for neural network dimension analysis, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp.3313-3318, 2008.
DOI : 10.1109/IJCNN.2008.4634268

H. Chandrasekaran, H. H. Chen, and M. T. Maury, Pruning of basis functions in nonlinear approximators, Neurocomputing, vol.34, issue.1-4, pp.29-53, 2000.
DOI : 10.1016/S0925-2312(00)00311-8

F. Gruau, A learning and pruning algorithm for genetic boolean neural networks, European Symp. on Artificial Neural Network ESANN'93, pp.57-63, 1993.

D. W. Ruck, S. K. Rogers, and M. Kabrisky, Feature selection using a multilayer perceptron, Neural Network Computing, vol.2, issue.2, pp.40-48, 1990.

G. Tarr, Multilayered feedforward networks for image segmentation, Ph.D. dissertation, Air Force Inst, 1991.

L. Ljung, System identification: theory for the users, N.J, 1987.

P. Thomas and G. Bloch, Initialization of one hidden layer feedforward neural networks for non-linear system identification, Proc. of the 15 th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics WC'97, pp.295-300, 1997.

H. Haouzi, Approche méthodologique pour l'intégration des systèmes contrôlés par le produit dans un environnement de juste-àtemps: Application à l'entreprise TRANE, 2008.

T. Klein, Le kanban actif pour assurer l'intéropérabilité décisionnelle centralisé/distribué: Application à un industriel de l'ameublement, 2008.