The MIN PFS problem and piecewise linear model estimation, Discrete Applied Mathematics, vol.118, issue.1-2, pp.115-143, 2002. ,
DOI : 10.1016/S0166-218X(01)00260-8
Theory of reproducing kernels. Transactions of the, pp.337-404, 1950. ,
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.174-188, 2002. ,
DOI : 10.1109/78.978374
Voronoi diagrams---a survey of a fundamental geometric data structure, ACM Computing Surveys, vol.23, issue.3, pp.345-405, 1991. ,
DOI : 10.1145/116873.116880
Identification of switched linear systems via sparse optimization, Automatica, vol.47, issue.4, pp.668-677, 2011. ,
DOI : 10.1016/j.automatica.2011.01.036
URL : https://hal.archives-ouvertes.fr/hal-00584246
A recursive identification algorithm for switched linear/affine models, Nonlinear Analysis: Hybrid Systems, vol.5, issue.2, pp.242-253, 2011. ,
DOI : 10.1016/j.nahs.2010.05.003
An 0 -1 norm based optimization procedure for the identification of switched nonlinear systems, Proceedings of the 49th IEEE International Conference on Decision and Control, pp.4467-4472, 2010. ,
Identification of mimo switched statespace models, Proceedings of American Control Conference, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00798991
On-line structured subspace identification with application to switched linear systems, International Journal of Control, vol.82, issue.8, pp.1496-1515, 2009. ,
DOI : 10.1109/97.410547
Algebraic identification of MIMO SARX models. Hybrid Systems: Computation and Control, pp.43-57, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00280409
Feature vector selection and projection using kernels, Neurocomputing, vol.55, issue.1-2, pp.21-38, 2003. ,
DOI : 10.1016/S0925-2312(03)00429-6
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.626
A bounded-error approach to piecewise affine system identification, IEEE Transactions on Automatic Control, issue.10, pp.501567-1580, 2005. ,
Control of systems integrating logic, dynamics, and constraints, Automatica, vol.35, issue.3, pp.407-428, 1999. ,
DOI : 10.1016/S0005-1098(98)00178-2
Robust linear programming discrimination of two linearly inseparable sets, Optimization Methods and Software, vol.1, issue.1, pp.23-34, 1992. ,
DOI : 10.1080/10556789208805504
Multicategory discrimination via linear programming. Optimization Methods and Software, pp.27-39, 1994. ,
ITERATIVE SUBSPACE IDENTIFICATION OF PIECEWISE LINEAR SYSTEMS, Proceedings of the 14th IFAC Symp. on System Identification, pp.368-373, 2006. ,
DOI : 10.3182/20060329-3-AU-2901.00054
Large scale kernel machines, 2007. ,
Identification of piecewise affine systems based on Dempster-Shafer Theory, Proceeding of 15th IFAC Symposium on System Identification, pp.1662-1667, 2009. ,
DOI : 10.3182/20090706-3-FR-2004.00276
Convex optimization, 2004. ,
Introduction to hybrid systems. Handbook of Networked and Embedded Control Systems, pp.91-116, 2005. ,
Hinging hyperplanes for regression, classification, and function approximation, IEEE Transactions on Information Theory, vol.39, issue.3, pp.999-1013, 1993. ,
DOI : 10.1109/18.256506
LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, 2001. ,
DOI : 10.1145/1961189.1961199
Compressive sampling, Proceedings of the International Congress of Mathematicians: Madrid, pp.1433-1452, 2006. ,
DOI : 10.4171/022-3/69
Decoding by Linear Programming, IEEE Transactions on Information Theory, vol.51, issue.12, pp.4203-4215, 2005. ,
DOI : 10.1109/TIT.2005.858979
Enhancing Sparsity by Reweighted ??? 1 Minimization, Journal of Fourier Analysis and Applications, vol.7, issue.3, pp.877-905, 2008. ,
DOI : 10.1007/s00041-008-9045-x
Efficient formation of a basis in a kernel induced feature space, Proceedings of the European Symposium on Artificial Neural Networks, pp.1-6, 2002. ,
Reduced rank kernel ridge regression, Neural Processing Letters, vol.16, issue.3, pp.293-302, 2002. ,
DOI : 10.1023/A:1021798002258
Neural networks for optimization and signal processing, 1993. ,
Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995. ,
DOI : 10.1007/BF00994018
Introduction to compressed sensing. Compressed Sensing: Theory and Applications, 2012. [31] B. De Schutter and B. De Moor. The extended linear complementarity problem and the modeling and analysis of hybrid systems, pp.635-636, 1999. ,
Model predictive control for max-plus-linear discrete event systems, Automatica, vol.37, issue.7, pp.1049-1056, 2001. ,
DOI : 10.1016/S0005-1098(01)00054-1
Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006. ,
DOI : 10.1109/TIT.2006.871582
URL : https://hal.archives-ouvertes.fr/inria-00369486
Optimally sparse representation in general (nonorthogonal) dictionaries via ??1 minimization, Proceedings of the National Academy of Sciences, pp.2197-2202, 2003. ,
DOI : 10.1073/pnas.0437847100
Uncertainty principles and ideal atomic decomposition, IEEE Transactions on Information Theory, vol.47, issue.7, pp.2845-2862, 1999. ,
DOI : 10.1109/18.959265
Hinging hyperplane trees for approximation and identification, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), pp.1266-1271, 1998. ,
DOI : 10.1109/CDC.1998.758452
Regularization networks and support vector machines, Advances in Computational Mathematics, vol.13, issue.1, pp.1-50, 2000. ,
DOI : 10.1023/A:1018946025316
Nonlinear System Identification using Structured Kernel Based Models, 2013. ,
A clustering technique for the identification of piecewise affine systems, Automatica, vol.39, issue.2, pp.205-217, 2003. ,
DOI : 10.1016/S0005-1098(02)00224-8
Efficient svm training using low-rank kernel representations, The Journal of Machine Learning Research, vol.2, pp.243-264, 2002. ,
Compressive Sensing, Handbook of Mathematical Methods in Imaging, pp.187-229, 2011. ,
DOI : 10.1007/978-3-642-27795-5_6-5
A survey on switched and piecewise affine system identification, Proceedings of the 16th IFAC Symposium on System Identification, pp.344-355, 2012. ,
DOI : 10.3182/20120711-3-BE-2027.00332
Segmentation algorithms for time series and sequence data, The SIAM International Conference on Data Mining: A Tutorial, 2005. ,
Sparse representations in unions of bases, IEEE Transactions on Information Theory, vol.49, issue.12, pp.3320-3325, 2003. ,
DOI : 10.1109/TIT.2003.820031
URL : https://hal.archives-ouvertes.fr/inria-00570057
Recursive Identification of Switched ARX Models with Unknown Number of Models and Unknown Orders, Proceedings of the 44th IEEE Conference on Decision and Control, pp.6115-6121, 2006. ,
DOI : 10.1109/CDC.2005.1583140
Linear complementarity systems, SIAM Journal on Applied Mathematics, vol.60, issue.4, pp.1234-1269, 2000. ,
URL : https://hal.archives-ouvertes.fr/hal-00834580
Equivalence of hybrid dynamical models, Automatica, vol.37, issue.7, pp.1085-1091, 2001. ,
DOI : 10.1016/S0005-1098(01)00059-0
Identification of hybrid linear time-invariant systems via subspace embedding and segmentation, Proceedings of the 43rd IEEE Conference on Decision and Control, pp.3227-3234, 2005. ,
Global optimization by multilevel coordinate search, Journal of Global Optimization, vol.14, issue.4, pp.331-355, 1999. ,
DOI : 10.1023/A:1008382309369
Principal component analysis, 2005. ,
DOI : 10.1007/978-1-4757-1904-8
Comparison of Four Procedures for the Identification of Hybrid Systems, Hybrid Systems: Computation and Control, pp.354-369, 2005. ,
DOI : 10.1007/978-3-540-31954-2_23
A Bayesian approach to identification of hybrid systems, IEEE Transactions on Automatic Control, vol.50, issue.10, pp.1520-1533, 2005. ,
DOI : 10.1109/TAC.2005.856649
Least Squares Support Vector Machines, World Scientific, 2002. ,
DOI : 10.1142/5089
Weighted 1 minimization for sparse recovery with prior information, IEEE International Symposium on Information Theory, pp.483-487, 2009. ,
Linear dependency between ?? and the input noise in ??-support vector regression, IEEE Transactions on Neural Networks, vol.14, issue.3, pp.544-553, 2003. ,
DOI : 10.1109/TNN.2003.810604
Identification and control of nonlinear systems via piecewise affine approximation, 49th IEEE Conference on Decision and Control (CDC), pp.6395-6402, 2010. ,
DOI : 10.1109/CDC.2010.5717032
Estimating the probability of success of a simple algorithm for switched linear regression. Nonlinear Analysis: Hybrid Systems, pp.31-47, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00743954
Switched and PieceWise Nonlinear Hybrid System Identification, Proceedings of the 11th International Conference on Hybrid Systems: Computation and Control, pp.330-343, 2008. ,
DOI : 10.1007/978-3-540-78929-1_24
URL : https://hal.archives-ouvertes.fr/hal-00203121
Nonlinear hybrid system identification with kernel models, 49th IEEE Conference on Decision and Control (CDC), pp.696-701, 2010. ,
DOI : 10.1109/CDC.2010.5718011
URL : https://hal.archives-ouvertes.fr/hal-00514429
A continuous optimization framework for hybrid system identification, Automatica, vol.47, issue.3, pp.608-613, 2011. ,
DOI : 10.1016/j.automatica.2011.01.020
URL : https://hal.archives-ouvertes.fr/hal-00559369
System identification: theory for the user, 1999. ,
Handbook of hybrid systems control: theory, tools, applications Identification of deterministic switched ARX systems via identification of algebraic varieties, Proceedings of the 8th International Conference on Hybrid Systems: Computation and Control, pp.449-465, 2005. ,
DOI : 10.1017/CBO9780511807930
Some methods for classification and analysis of multivariate observations, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967. ,
Large scale kernel regression via linear programming, Machine Learning, pp.255-269, 2002. ,
Identification of multiple mode models via Distributed Particle Swarm Optimization, Proceedings of the 18th IFAC World Congress, pp.7743-7748, 2011. ,
DOI : 10.3182/20110828-6-IT-1002.02438
Sparse Identification of Nonlinear Functions and Parametric Set Membership Optimality Analysis, IEEE Transactions on Automatic Control, vol.57, issue.12, pp.3236-3241, 2012. ,
DOI : 10.1109/TAC.2012.2202051
Piecewise affine system identification using sum-ofnorms regularization, Proceedings of the 18th IFAC World Congress, pp.6640-6645, 2011. ,
DOI : 10.3182/20110828-6-it-1002.00611
URL : http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-60984
Identification of switched linear regression models using sum-of-norms regularization, Automatica, vol.49, issue.4, p.2013 ,
DOI : 10.1016/j.automatica.2013.01.031
Segmentation of ARX-models using sum-of-norms regularization, Automatica, vol.46, issue.6, pp.1107-1111, 2010. ,
DOI : 10.1016/j.automatica.2010.03.013
Robust identification of switched affine systems via moments-based convex optimization, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, pp.4686-4691, 2009. ,
DOI : 10.1109/CDC.2009.5399962
A Sparsification Approach to Set Membership Identification of Switched Affine Systems, IEEE Transactions on Automatic Control, vol.57, issue.3, pp.634-648, 2012. ,
DOI : 10.1109/TAC.2011.2166295
Identification of Hybrid Systems A Tutorial, European Journal of Control, vol.13, issue.2-3, pp.242-260, 2007. ,
DOI : 10.3166/ejc.13.242-260
Identification of switching systems using change detection technique in the subspace framework, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), pp.3720-3725, 2004. ,
DOI : 10.1109/CDC.2004.1429317
URL : https://hal.archives-ouvertes.fr/hal-00510894
Fast training of support vector machines using sequential minimal optimization, Advances in Kernel Methods: Support Vector Learning, pp.185-208, 1999. ,
On the hinge-finding algorithm for hingeing hyperplanes, IEEE Transactions on Information Theory, vol.44, issue.3, pp.1310-1319, 1998. ,
DOI : 10.1109/18.669422
Identification of piecewise affine systems via mixed-integer programming, Automatica, vol.40, issue.1, pp.37-50, 2004. ,
DOI : 10.1016/j.automatica.2003.08.006
Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression, Neural Computing & Applications, vol.10, issue.3, pp.231-243, 2001. ,
DOI : 10.1007/s521-001-8051-z
A Generalized Representer Theorem, Computational Learning Theory, pp.416-426, 2001. ,
DOI : 10.1007/3-540-44581-1_27
Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998. ,
DOI : 10.1007/BF02281970
New Support Vector Algorithms, Neural Computation, vol.20, issue.5, pp.1207-1245, 2000. ,
DOI : 10.1016/S0893-6080(98)00032-X
Improvements to the SMO algorithm for SVM regression, IEEE Transactions on Neural Networks, vol.11, issue.5, pp.1188-1193, 2000. ,
DOI : 10.1109/72.870050
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
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 tutorial on support vector regression, Statistics and Computing, vol.14, issue.3, pp.199-222, 2004. ,
DOI : 10.1023/B:STCO.0000035301.49549.88
System Identification, Journal of Dynamic Systems, Measurement, and Control, vol.115, issue.4, 1988. ,
DOI : 10.1115/1.2899207
Nonlinear regulation: The piecewise linear approach, IEEE Transactions on Automatic Control, vol.26, issue.2, pp.346-358, 1981. ,
DOI : 10.1109/TAC.1981.1102596
Available at http://svmlight.joachims.org, 1998. ,
A clusteringbased bounded-error approach for identification of PWA hybrid systems, Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision, pp.1-6, 2006. ,
A Modified k-plane Clustering Algorithm for Identification of Hybrid Systems, 2006 6th World Congress on Intelligent Control and Automation, pp.1333-1337, 2006. ,
DOI : 10.1109/WCICA.2006.1712564
Modified support vector machines in financial time series forecasting, Neurocomputing, vol.48, issue.1-4, pp.847-861, 2002. ,
DOI : 10.1016/S0925-2312(01)00676-2
Unsupervised feature extraction via kernel subspace techniques, Neurocomputing, vol.74, issue.5, pp.820-830, 2011. ,
DOI : 10.1016/j.neucom.2010.11.011
URL : http://ria.ua.pt/bitstream/10773/5289/1/neurocompoting2011.pdf
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit, IEEE Transactions on Information Theory, vol.53, issue.12, pp.4655-4666, 2007. ,
DOI : 10.1109/TIT.2007.909108
URL : http://authors.library.caltech.edu/9490/1/TROieeetit07.pdf
Complementarity modeling of hybrid systems, IEEE Transactions on Automatic Control, vol.43, issue.4, pp.483-490, 1998. ,
DOI : 10.1109/9.664151
The nature of statistical learning theory, 1995. ,
The Nature of Statistical Learning Theory, 1995. ,
Subspace identification of piecewise linear systems, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), pp.3838-3843, 2005. ,
DOI : 10.1109/CDC.2004.1429336
Identification of PWARX hybrid models with unknown and possibly different orders, Proceedings of the American Control Conference, pp.547-552, 2004. ,
Recursive identification of switched ARX systems, Automatica, vol.44, issue.9, pp.2274-2287, 2008. ,
DOI : 10.1016/j.automatica.2008.01.025
Subspace Clustering, IEEE Signal Processing Magazine, vol.28, issue.2, pp.52-68, 2011. ,
DOI : 10.1109/MSP.2010.939739
Recursive identification of switched ARX hybrid models: exponential convergence and persistence of excitation, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), pp.32-37, 2005. ,
DOI : 10.1109/CDC.2004.1428602
Generalized principal component analysis (GPCA), IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.12, pp.1945-1959, 2005. ,
DOI : 10.1109/TPAMI.2005.244
URL : http://arxiv.org/abs/1202.4002
An algebraic geometric approach to the identification of a class of linear hybrid systems, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475), pp.167-172, 2003. ,
DOI : 10.1109/CDC.2003.1272554
Identification of Switched Linear Systems, 2013. ,
Adaptive hinging hyperplanes and its applications in dynamic system identification, Automatica, vol.45, issue.10, pp.2325-2332, 2009. ,
DOI : 10.1016/j.automatica.2009.06.013
Reweighted $\ell_1$-Minimization for Sparse Solutions to Underdetermined Linear Systems, SIAM Journal on Optimization, vol.22, issue.3, pp.1065-1088, 2012. ,
DOI : 10.1137/110847445
un modèle du système est la pierre angulaire des procédures comme la synthèse d'une commande, la détection des défaillances, la prédiction... Cette thèse traite de l'identification d'une classe de systèmes complexes, les systèmes dynamiques hybrides. Ces systèmes impliquent l'interaction de comportements continus et discrets ,