. Abry, Multiscale nature of network traffic, IEEE Signal Processing Magazine, vol.19, issue.3, 2002.
DOI : 10.1109/79.998080

Y. S. Abu-mostafa-abu-mostafa, Learning from hints in neural networks, Journal of Complexity, vol.6, issue.2, pp.192-198, 1990.
DOI : 10.1016/0885-064X(90)90006-Y

Y. S. Abu-mostafa-abu-mostafa, Hints, Neural Computation, vol.7, issue.4, pp.639-671, 1995.
DOI : 10.1142/S0129065790000102

. Ahuja, Network Flows -Theory, Algorithms, and Applications, 1993.

. Ahuja, Networks Flows: Theory , Algorithms and Applications, 1993.

S. Amari, Natural Gradient Works Efficiently in Learning, Neural Computation, vol.37, issue.2, pp.251-276, 1998.
DOI : 10.1103/PhysRevLett.76.2188

. Amari, Adaptive Method of Realizing Natural Gradient Learning for Multilayer Perceptrons, Neural Computation, vol.12, issue.6, pp.1399-1409, 2000.
DOI : 10.1162/089976698300017007

P. Ampazis, N. Ampazis, and S. Perantonis, Levenberg-Marquadt algorithm with adaptative momentum for the efficient training of feedforward networks, Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN'00), 2000.

. Anick, Stochastic Theory of a Data-Handling System with Multiple Sources, Bell System Technical Journal, vol.61, issue.8, pp.611871-1984, 1982.
DOI : 10.1002/j.1538-7305.1982.tb03089.x

. Apostolopoulos, QoS Routing Mechanisms and OSPF Extensions, RFC, vol.2676, 1999.
DOI : 10.17487/rfc2676

A. Aussem, Call admission control in ATM networks with the random neural network, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), pp.2482-2487, 1994.
DOI : 10.1109/ICNN.1994.374610

A. Aussem, Théorie et applications des réseaux de neurones récurrents et dynamiques à la prédiction, à la modélisation et au contrôle adaptatif des processus dynamiques, Thèse de doctorat, 1995.

A. Aussem, Le calcul du gradient d'erreur dans les réseaux de neurones discrets bouclés à délais: applications aux Télécom et aux Sciences Environnementales. Habilitation à diriger des recherches, 2002.

. Aussem, Queueing network modelling with distributed neural networks for service quality estimation in B-ISDN networks, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000.
DOI : 10.1109/IJCNN.2000.861501

M. Aussem, A. Aussem, and F. Murtagh, Web traffic demand forecasting using wavelet-based multiscale decomposition, International Journal of Intelligent Systems, vol.10, issue.2, pp.215-236, 2001.
DOI : 10.1002/1098-111X(200102)16:2<215::AID-INT50>3.0.CO;2-#
URL : https://hal.archives-ouvertes.fr/hal-00395455

. Aussem, Neural-based queueing system modelling for service quality estimation in B-ISDN networks, 9th International Conference on Artificial Neural Networks: ICANN '99, pp.970-975, 1999.
DOI : 10.1049/cp:19991238

M. H. Austern, Generic Programming and the STL: Using and Extending the C++ Standard Template Library, 1999.

B. Bachelet, Modélisation et Optimisation de problèmes de synchronisation dans les documents hypermédia, 2003.

. Bachelet, Designing generic algorithms for operations research. Software: Practice and Experience, 2006.
DOI : 10.1002/spe.682
URL : https://hal.archives-ouvertes.fr/hal-00107137

. Baier, On the k-splittable flow problem, Proceedings of the 10th Annual European Symposium on Algorithms, pp.101-113, 2002.

. Banks, Discrete-Event System Simulation, 1996.

. Barford, . Plonka, P. Barford, and D. Plonka, Characteristics of network traffic flow anomalies, Proceedings of the First ACM SIGCOMM Workshop on Internet Measurement , IMW '01, 2001.
DOI : 10.1145/505202.505211

R. Battiti, First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method, Neural Computation, vol.8, issue.3, pp.141-166, 1992.
DOI : 10.1162/neco.1989.1.4.425
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.482

. Bennani, . Bossaert, Y. Bennani, and F. Bossaert, Predictive neural networks for traffic disturbance detection in the telephone network, Proceedings of the IMACS Multiconference in Computational Engineering in Systems Applications (IMACS-CESA'96), 1996.

G. Bertsekas, D. Bertsekas, and R. Gallager, Data Networks, second edition, 1992.

D. P. Bertsekas, Constrained Optimization and Lagrange Multiplier Methods, 1982.

D. P. Bertsekas, Nonlinear Programming, second edition, Athena Scien- tific, 1999.

. Bienstock, Minimum cost capacity installation for multicommodity network flows, Mathematical Programming, vol.68, issue.1, pp.177-199, 1998.
DOI : 10.1007/BF01581104
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.2430

. Bienstock, . Raskina, D. Bienstock, and O. Raskina, Analysis of the flow deviation method for the max concurrent flow problem, 2002.

C. M. Bishop, Curvature-driven smoothing: a learning algorithm for feedforward networks, IEEE Transactions on Neural Networks, vol.4, issue.5, 1993.
DOI : 10.1109/72.248466

C. M. Bishop, Neural Networks for Pattern Recognition, 1995.

. Blake, An Architecture for Differentiated Service, 1998.
DOI : 10.17487/rfc2475

M. Bolla, R. Bolla, and P. Maryni, An adaptive neural network admission controller for dynamic bandwidth allocation, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.28, issue.4, pp.592-601, 1998.
DOI : 10.1109/3477.704298

L. Boyan, J. A. Boyan, and M. L. Littman, Packet routing in dynamically changing networks: A reinforcement learning approach, Advances in Neural Information Processing Systems, vol.6, pp.671-678, 1993.

. Braden, Resource ReSerVation Protocol (RSVP) ? Version 1 Functional Specification, p.3936, 1997.
DOI : 10.17487/rfc2205

. Brandt, A hybrid neural approach to ATM admission control, Proceedings of the XV International Switching Symposium (ISS'95), pp.283-287, 1995.

. Bronstein, Self-aware services: using Bayesian networks for detecting anomalies in Internet-based services, 2001 IEEE/IFIP International Symposium on Integrated Network Management Proceedings. Integrated Network Management VII. Integrated Management Strategies for the New Millennium (Cat. No.01EX470), 2001.
DOI : 10.1109/INM.2001.918070

. Bruce, A statically safe alternative to virtual types, ECOOP'98 -Lecture Notes in Computer Science, pp.523-549, 1998.
DOI : 10.1007/BFb0054106

. Bu, On routing table growth, Proceedings of Globe Internet, 2002.
DOI : 10.1145/510726.510751

J. Cannady, Artificial neural networks for misuse detection, Proceedings of the National Information Systems Security Conference (NISSC'98), pp.443-456, 1998.

C. W. Carroll, The Created Response Surface Technique for Optimizing Nonlinear, Restrained Systems, Operations Research, vol.9, issue.2, pp.169-184, 1961.
DOI : 10.1287/opre.9.2.169

. Caseau, CLAIRE: combining sets, search and rules to better express algorithms, Theory and Practice of Logic Programming, pp.2-6, 2002.
DOI : 10.1017/S1471068401001363

. Casilari, Neural modelling of Ethernet traffic over ATM networks, Proceedings of EANN'98, pp.304-307, 1998.

C. , Z. Chang, C. Zajic, and T. , Effective bandwidths of departure processes from queues with time varying capacities, Proceedings of the Conference of the IEEE Communications Society (INFOCOM 1995), pp.1001-1009, 1995.

. Clérot, Using neural networks for predicting transient leaky bucket characterisations of data traffic. Applications to dynamic resource allocation in ATM networks, Proceedings of 6th IFIP Workshop on Performance Modelling and Evaluation of ATM Networks (IFIP ATM'98), 1998.

R. Courant, Variational methods for the solution of problems of equilibrium and vibration. Bulletin of the, pp.1-23, 1943.

. Cui, oStream: Asynchronous Streaming Multicast in Application-Layer Overlay Networks, IEEE Journal on Selected Areas in Communications, vol.22, issue.1, pp.91-106, 2004.
DOI : 10.1109/JSAC.2003.818799

G. V. 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

D. Decoste, Bounds estimation via regression with asymmetric cost functions Japan, International Joint Conference on Artificial Intelligence (IJCAI'97, 1997.

C. Duhamel, Un cadre formel pour les méthode d'amélioration itératives. applications à deux problèmes d'optimisation dans les réseaux, Thèse de doctorat, 2001.

. Duhamel, Routing with neuralbased QoS constraints, Proceedings of the first International Network Optimization Conference (INOC'2003), pp.201-206, 2003.

. Duhamel, Minimizing congestion with a bounded number of paths, Algotel, 2003.

. Duret-lutz, Generic Design Patterns in C++, 6th USENIX Conference on Object-Oriented Technologies and Systems, pp.189-202, 2001.

H. Fdida, S. Fdida, and G. Hébuterne, Méthodes exactes d'analyse de performance des réseaux, Hermes Science, 2004.

V. Fineberg, QoS support in MPLS networks, 2003.

R. Fletcher, Penalty functions The State of the Art, Mathematical Programming, pp.87-114, 1982.

. Ford, . Fulkerson, L. R. Ford, and D. R. Fulkerson, A Suggested Computation for Maximal Multi-Commodity Network Flows, Management Science, vol.5, issue.1, pp.97-101, 1958.
DOI : 10.1287/mnsc.5.1.97

. Forster, The Graph Template Li- brary, 1999.

. Fratta, The flow deviation method: An approach to store-and-forward communication network design, Networks, vol.1, issue.2, pp.97-133, 1973.
DOI : 10.1002/net.3230030202

K. Frisch, The logarithmic potential method of convex programming, 1955.

. Gamma, Design Patterns:Elements of Reusable Object-Oriented Software, 1995.

. Garcia, Modélisation différentielle et simulation hybride distribuée, Calculateurs Parallèles, Réseaux et Systèmes Répartis, vol.13, issue.6, pp.635-664, 2001.

. Garcia, A Comparative Study of Language Support for Generic Programming, Proceedings of the 18th ACM SIGPLAN Conference OOSPLA, 2003.

E. Gelenbe, Random Neural Networks with Negative and Positive Signals and Product Form Solution, Neural Computation, vol.1, issue.4, pp.502-511, 1989.
DOI : 10.1162/neco.1989.1.4.502

E. Gelenbe, Learning in the Recurrent Random Neural Network, Neural Computation, vol.85, issue.1, pp.154-164, 1993.
DOI : 10.1162/neco.1989.1.2.161

. Gelenbe, Design and performance of cognitive packet networks, Performance Evaluation, vol.46, issue.2-3, pp.155-176, 2001.
DOI : 10.1016/S0166-5316(01)00042-6

. Gelenbe, Measurement and performance of a cognitive packet network, Computer Networks, vol.37, issue.6, pp.691-701, 2001.
DOI : 10.1016/S1389-1286(01)00253-5

E. Gelenbe and A. Núñez, Self-Aware Networks and Quality of Service, Proceedings of the 13th International Conference on Artificial Neural Networks (ICANN'03), pp.901-908, 2003.
DOI : 10.1007/3-540-44989-2_107

. Gelenbe, E. Pujolle-]-gelenbe, and G. Pujolle, Introduction to Queueing Networks, 1998.

S. Gellman, M. Gellman, and P. Su, Using adaptive routing to achieve quality of service, Performance Evaluation, vol.57, issue.2, pp.105-119, 2004.

. Geman, Neural Networks and the Bias/Variance Dilemma, Neural Computation, vol.36, issue.1, pp.1-58, 1992.
DOI : 10.1162/neco.1990.2.1.1

. Gourdin, Optimisation des réseaux de télécommunications, pp.8-12, 2004.

C. Goutte, Apprentissage statistisque et régularisation pour la régression, Thèse de doctorat, 1997.

. Grossglauser, . Bolot, M. Grossglauser, and J. Bolot, On the relevance of long-range dependence in network traffic, IEEE/ACM Transactions on Networking, vol.7, issue.5, pp.629-640, 1999.
DOI : 10.1109/90.803379
URL : https://hal.archives-ouvertes.fr/inria-00073861

P. Guérin, R. Guérin, and V. Peris, Quality-of-service in packet networks: basic mechanisms and directions, Computer Networks, vol.31, issue.3, pp.169-189, 1999.
DOI : 10.1016/S0169-7552(98)00261-X

P. Guermeur, Y. Guermeur, and H. Paugam-moisy, Apprentissage automatique, chapter Théorie de l'apprentissage de Vapnik et SVM, pp.109-138, 1999.

R. Gusella, Characterizing the variability of arrival processes with indexes of dispersion, IEEE Journal on Selected Areas in Communications, vol.9, issue.2, pp.203-211, 1991.
DOI : 10.1109/49.68448

I. W. Habib, Applications of neurocomputing in traffic management of ATM networks, Proceedings of the IEEE, pp.1430-1441, 1996.
DOI : 10.1109/5.537109

. Hadjiefthymiades, Using path prediction to improve TCP performance in wireless/mobile communications, IEEE Communications Magazine, vol.40, issue.8, pp.4054-61, 2002.
DOI : 10.1109/MCOM.2002.1024415

L. Heffes, H. Heffes, and D. M. Lucantoni, A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance, IEEE Journal on Selected Areas in Communications, vol.4, issue.6, pp.856-868, 1986.
DOI : 10.1109/JSAC.1986.1146393

A. Hiramatsu, ATM communications network control by neural networks, IEEE Transactions on Neural Networks, vol.1, issue.1, pp.122-130, 1990.
DOI : 10.1109/72.80211

A. Hiramatsu, Training techniques for neural network applications in ATM, IEEE Communications Magazine, vol.33, issue.10, pp.58-67, 1995.
DOI : 10.1109/35.466221

J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of Sciences of the United States of America, issue.8, pp.792554-2558, 1982.

. Incera, FluidSim: A tool to simulate fluid models of high-speed networks, Performence Evaluation, vol.44, pp.1-425, 2001.

R. Izquierdo, M. R. Izquierdo, and D. S. Reeves, A survey of statistical source models for variable-bit-rate compressed video, Multimedia Systems, vol.7, issue.3, pp.199-213, 1999.
DOI : 10.1007/s005300050122

. Johnson, . Zweig, R. E. Johnson, and J. Zweig, Delegation in C++, Journal of Object-Oriented Programming, pp.22-35, 1991.

. Karpinski, . Macintyre, M. Karpinski, and A. Macintyre, Polynomial bounds for VC dimension of sigmoidal neural networks, Proceedings of the twenty-seventh annual ACM symposium on Theory of computing , STOC '95, pp.200-208, 1995.
DOI : 10.1145/225058.225118

F. Kelly, Stochastic Networks: Theory and Applications, chapter Notes on Effective Bandwidths, pp.141-168, 1996.

L. Kleinrock, Communication Nets: Stochastic Message Flow and Delay, 1964.

L. Kleinrock, Queueing Systems, Volume I: Theory, 1975.

. Koiran, P. Sontag-]-koiran, and E. D. Sontag, Neural Networks with Quadratic VC Dimension, Journal of Computer and System Sciences, vol.54, issue.1, pp.190-198, 1997.
DOI : 10.1006/jcss.1997.1479
URL : http://doi.org/10.1006/jcss.1997.1479

F. Krief, Self-aware management of IP networks with QoS guarantees, International Journal of Network Management, vol.14, issue.5, pp.351-364, 2004.
DOI : 10.1002/nem.532
URL : https://hal.archives-ouvertes.fr/hal-00351752

. Krogh, . Hertz, A. Krogh, and J. A. Hertz, A simple weight decay can improve generalization, Advances in Neural Information Processing Systems, pp.950-957, 1992.

V. Kulkarni, Frontiers of Queueing Systems, chapter Fluid models for single buffer systems, pp.321-339, 1997.

S. Lampinen, J. Lampinen, and A. Selonen, Multilayer perceptron training with inaccurate derivative information, Proceedings of ICNN'95, International Conference on Neural Networks, pp.2811-2815, 1995.
DOI : 10.1109/ICNN.1995.488178
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.6703

S. Lampinen, J. Lampinen, and A. Selonen, Using background knowledge in multilayer perceptron learning, Proceedings of the 10th Scandinavian Conference on Image Analysis (SCIA'97, pp.545-549, 1997.

L. Lasdon, Optimization Theory of Large Systems, IEEE Transactions on Systems, Man, and Cybernetics, vol.1, issue.3, 1970.
DOI : 10.1109/TSMC.1971.4308301

. Lee, . Hou, S. Lee, and C. Hou, A neural-fuzzy system for congestion control in ATM networks, IEEE Transactions on Systems, Man and Cymernetics -Part B: Cybernetics, vol.30, issue.1, pp.2-9, 2000.

C. Lemaréchal, Lagrangian Relaxation, Computational Combinatorial Optimization, pp.115-160, 2001.
DOI : 10.1007/3-540-45586-8_4

S. B. Lippman, Inside the C++ Object Model, 1996.

S. Macintyre, A. Macintyre, and E. Sontag, Finiteness results for sigmoidal ???neural??? networks, Proceedings of the twenty-fifth annual ACM symposium on Theory of computing , STOC '93, pp.325-334, 1993.
DOI : 10.1145/167088.167192
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.6275

A. Mahul, A. Mahul, and A. Aussem, Distributed Neural Networks for Quality of Service Estimation in Communication Networks, International Journal of Computational Intelligence and Applications, vol.03, issue.03, pp.297-308, 2003.
DOI : 10.1142/S1469026803000999
URL : https://hal.archives-ouvertes.fr/hal-00395432

A. Mahul, A. Mahul, and A. Aussem, Neural-based quality of service estimation in MPLS routers, Supplementary Proceedings of the International Conference on Artificial Neural Networks (ICANN'2003), pp.390-393, 2003.

A. Mahul, A. Mahul, and A. Aussem, Training feed-forward neural networks with monotonicity requirements, 2004.

P. Maiorov, V. Maiorov, and A. Pinkus, Lower bounds for approximation by MLP neural networks, Neurocomputing, vol.25, issue.1-3, pp.81-91, 1999.
DOI : 10.1016/S0925-2312(98)00111-8
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.303.5508

I. Mc-loone, S. Loone, and G. Irwin, Improving neural network training solutions using regularisation, Neurocomputing, vol.37, issue.1-4, pp.71-90, 2001.
DOI : 10.1016/S0925-2312(00)00314-3

. Mcgregor, Flow Clustering Using Machine Learning Techniques, Proceedings of the Passive and Active Network Measurement: 5th International Workshop, 2004.
DOI : 10.1007/978-3-540-24668-8_21

B. Mcnamara and Y. Smaragdakis, Static Interfaces in C++, First Workshop on C++ Template Programming, 2000.

. Moh, Traffic prediction and dynamic bandwith allocation over ATM: a neural network approach, Computer Communications, issue.8, p.18, 1995.

M. F. Møller, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, vol.6, issue.4, pp.525-533, 1993.
DOI : 10.1016/S0893-6080(05)80056-5

M. Molnar, S. Molnar, and I. Maricza, Source Characterization in Broadband Networks, Vilamoura, Portugal. COST, vol.257, 1999.

Z. Moore, A. Moore, and D. Zuev, Internet traffic classification using bayesian analysis techniques, Proceedings of the ACM SIGMETRICS 2005, 2005.
DOI : 10.1145/1071690.1064220
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.2173

S. Musser, D. R. Musser, and A. A. Stepanov, Generic programming, In Lecture Notes in Computer Science, vol.358, pp.13-25, 1989.
DOI : 10.1007/3-540-51084-2_2

J. Nocedal, Theory of algorithms for unconstrained optimization, Acta Numerica, vol.14, pp.192-242, 1992.
DOI : 10.1137/0726042

J. Nocedal, Large scale unconstrained optimization, The State of the Art in Numerical Analysis, pp.311-338, 1997.

. Nordstrom, Statistical preprocessing for service quality estimation in a broadband network, Proceedings of the World Congress on Neural Networks (WCNN'93), 1993.

. Papaioannou, Efficient agent-based selection of DiffServ SLAs over MPLS networks within the ASP service model, Journal of Network and Systems Management, vol.10, issue.1, pp.63-89, 2002.
DOI : 10.1023/A:1014453525541

. Park, Adaptive natural gradient learning algorithms for various stochastic models, Neural Networks, vol.13, issue.7, pp.755-764, 2000.
DOI : 10.1016/S0893-6080(00)00051-4
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.7685

. Paxson, V. Floyd-]-paxson, and S. Floyd, Wide area traffic: the failure of Poisson modeling, IEEE/ACM Transactions on Networking, vol.3, issue.3, pp.226-244, 1995.
DOI : 10.1109/90.392383

. Perantonis, A constrained learning framework for feedforward neural networks, Annals of Operations Research, vol.99, issue.1/4, pp.385-401, 2000.
DOI : 10.1023/A:1019240304484

S. Peshkin, L. Peshkin, and V. Savova, Reinforcement learning for adaptive routing, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), pp.1825-1830, 2002.
DOI : 10.1109/IJCNN.2002.1007796

A. Pinkus, Approximation theory of the MLP model in neural networks, Acta Numerica, vol.18, pp.143-195, 1999.
DOI : 10.1162/neco.1994.6.6.1233

. Plaut, Experiments on learning by back-propagation, 1986.

G. Pujolle, Les Réseaux, 2000.

S. Rattray, M. Rattray, and D. Saad, Analysis of natural gradient descent for multilayer neural networks, Physical Review E, vol.59, issue.4, pp.4523-4532, 1999.
DOI : 10.1103/PhysRevE.59.4523

. Ritke, Contradictory relationship between hurst parameter and queueing performance, Proceedings of Symposium on Performance Evaluation of Computer Telecommunication Systems (SPECTS'99), 1999.

M. Robbins, H. Robbins, and S. Monro, A Stochastic Approximation Method, The Annals of Mathematical Statistics, vol.22, issue.3, pp.400-407, 1951.
DOI : 10.1214/aoms/1177729586

. Rosen, Multiprotocol Label Switching Architecture, 2001.
DOI : 10.17487/rfc3031

F. Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, 1962.

. Rumelhart, Learning representations by back-propagating errors, Nature, vol.85, issue.6088, pp.533-536, 1986.
DOI : 10.1038/323533a0

. Ryu, B. K. Elwalid-]-ryu, and A. Elwalid, The importance of long-range dependence of VBR video traffic in ATM traffic engineering: Myths and realities, Proceedings of the ACM SIGCOMM 1996, pp.3-14, 1996.

. Saarinen, Ill-Conditioning in Neural Network Training Problems, SIAM Journal on Scientific Computing, vol.14, issue.3, pp.693-714, 1993.
DOI : 10.1137/0914044

V. Salamatian, K. Salamatian, and S. Vaton, Hidden markov modeling for network communication channels, Proceedings of the ACM SIGMETRICS 2001, pp.92-101, 2001.

. Shen, Traffic modeling, prediction and congestion control in hight-speed networks: A fuzzy ar approach, IEEE Transactions on Fuzzy Systems, vol.8, issue.5, pp.491-508, 2000.

. Siek, The Boost Graph Library: User Guide and Reference Manual, 2002.

L. Siek, J. G. Siek, and A. Lumsdaine, Concept Checking: Binding Parametric Polymorphism in C++, First Workshop on C++ Template Programming, 2000.

J. Sill, Monotonic networks, Advances in Neural Information Processing Systems, pp.661-667, 1998.

A. Sill, J. Sill, and Y. S. Abu-mostafa, Monotonic hints, Advances in Neural Information Processing Systems, vol.9, p.634, 1997.

T. Soh, W. Soh, and C. Tham, Modular neural networks for multi-service connection admission control, Computer Networks, vol.36, issue.2-3, pp.181-202, 2001.
DOI : 10.1016/S1389-1286(00)00197-3

. Soltani, The long-term memory prediction by multiscale decomposition, Signal Processing, vol.80, issue.10, pp.2195-2205, 2000.
DOI : 10.1016/S0165-1684(00)00077-3

. Taqqu, Is network traffic selfsimilar or multifractal? Fractals, pp.63-74, 1997.
DOI : 10.1142/s0218348x97000073
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.9141

A. Tikhonov, A. Tikhonov, and V. Arsenin, Solutions of Ill-Posed Problems, 1977.

G. Tran-gia, P. Tran-gia, and O. Gropp, Performance of a neural net used as admission controller in ATM systems, [Conference Record] GLOBECOM '92, Communications for Global Users: IEEE, pp.1303-1309, 1992.
DOI : 10.1109/GLOCOM.1992.276603

V. Vapnik and A. Chervonenkis, The necessary and sufficient conditions for consistency in the empirical risk minimisation method, Pattern Recognition and Image Analysis, vol.1, issue.3, pp.283-305, 1991.

V. N. Vapnik, The Nature of Statistical Learning Theory, 1995.

V. N. Vapnik, Statistical Learning Theory, 1998.

B. Veres, A. Veres, and M. Boda, The chaotic nature of TCP congestion control, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), pp.1715-1723, 2000.
DOI : 10.1109/INFCOM.2000.832571

V. Kumar, V. Kumar, B. Venkataram, and P. , An LP-based admission control using artificial neural networks for integrated servioces in mobile networks, Wireless Personal Communications, vol.20, issue.3, pp.219-236, 2002.
DOI : 10.1023/A:1014442827569

Z. Wang, On the complexity of quality of service routing, Information Processing Letters, vol.69, issue.3, pp.111-114, 1999.
DOI : 10.1016/S0020-0190(98)00206-3

C. Watkins, Learning from Delayed Rewards, 1989.

P. Werbos, Beyond Regression: New Tolls for Prediction and Analysis in Behavioral Sciences, 1974.

. Wilson, D. R. Martinez-]-wilson, and T. R. Martinez, The general inefficiency of batch training for gradient descent learning, Neural Networks, vol.16, issue.10, pp.1429-1451, 2003.
DOI : 10.1016/S0893-6080(03)00138-2

. Yon, Distance Minimization in Public Transportation Networks with Elastic Demands: Exact Model and Approached Methods, 21st IFIP TC 7 Conference, 2003.

H. Zadeh, Neural network modeling of self-similar teletraffic patterns, Proceedings of the first Workshop on Fractals and Self-Similarity, ACM SIGKDD Conference, 2002.

H. Zadeh and E. A. Jonckheere, Dynamic neural-based buffer management for queuing systems with self-similar characteristics". to appear in IEEE Trans, on Neural Networks, Special Issue on Adaptive Learning Systems in Communication Networks, 2005.

Y. Yuan, Nonlinear optimization : Trust region algorithms, Proceedings of Chinese SIAM annual meeting, pp.83-97, 1994.

G. Loi-de, 51 momentum, p.123