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Статті в журналах з теми "Subspace identification methods":
Dalen, Christer, and David Di Ruscio. "On subspace system identification methods." Modeling, Identification and Control: A Norwegian Research Bulletin 43, no. 4 (2022): 119–30. http://dx.doi.org/10.4173/mic.2022.4.1.
Viberg, Mats. "Subspace Methods in System Identification." IFAC Proceedings Volumes 27, no. 8 (July 1994): 1–12. http://dx.doi.org/10.1016/s1474-6670(17)47689-0.
Joe Qin, S. "Subspace methods for system identification." Automatica 43, no. 4 (April 2007): 748–49. http://dx.doi.org/10.1016/j.automatica.2006.07.027.
Avcıoğlu, Sevil, Ali Türker Kutay, and Kemal Leblebicioğlu. "Identification of Physical Helicopter Models Using Subspace Identification." Journal of the American Helicopter Society 65, no. 2 (April 1, 2020): 1–14. http://dx.doi.org/10.4050/jahs.65.022001.
Miller, Daniel N., and Raymond A. de Callafon. "Subspace Identification From Classical Realization Methods." IFAC Proceedings Volumes 42, no. 10 (2009): 102–7. http://dx.doi.org/10.3182/20090706-3-fr-2004.00016.
Mathieu, Pouliquen, and M'Saad Mohammed. "AN INTERPRETATION OF SUBSPACE IDENTIFICATION METHODS." IFAC Proceedings Volumes 38, no. 1 (2005): 904–9. http://dx.doi.org/10.3182/20050703-6-cz-1902.00152.
Wani Jamaludin, Irma Wani Jamaludin, and Norhaliza Abdul Wahab. "Recursive Subspace Identification Algorithm using the Propagator Based Method." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 1 (April 1, 2017): 172. http://dx.doi.org/10.11591/ijeecs.v6.i1.pp172-179.
Mohd-Mokhtar, Rosmiwati, and Liuping Wang. "Continuous time system identification using subspace methods." ANZIAM Journal 48 (June 26, 2007): 712. http://dx.doi.org/10.21914/anziamj.v47i0.1072.
Muradore, Riccardo, and Enrico Fedrigo. "SUBSPACE IDENTIFICATION METHODS APPLIED TO ADAPTIVE OPTICS." IFAC Proceedings Volumes 39, no. 1 (2006): 943–48. http://dx.doi.org/10.3182/20060329-3-au-2901.00150.
van der Veen, Gijs, Jan-Willem van Wingerden, Marco Lovera, Marco Bergamasco, and Michel Verhaegen. "Closed-loop subspace identification methods: an overview." IET Control Theory & Applications 7, no. 10 (July 4, 2013): 1339–58. http://dx.doi.org/10.1049/iet-cta.2012.0653.
Дисертації з теми "Subspace identification methods":
Shi, Ruijie. "Subspace identification methods for process dynamic modeling /." *McMaster only, 2001.
Zhao, Yong. "Identification of ankle joint stiffness using subspace methods." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86800.
L'étude de la rigidité articulaire en réponse à une charge est un problème difficile car les couples réflexes et intrinsèques ne peuvent pas être mesurés séparément expérimentalement. En outre, la rigidité articulaire opère en boucle fermée car le couple de la cheville est réinjectée à travers la charge pour modifier la position de la cheville. Dans cette thèse, un modèle d'espace d'état pour la rigidité articulaire de la cheville est développé. Une méthode sous-espace à temps discret est ensuite utilisée pour estimer ce modèle d'espace d'état pour la rigidité globale. En considérant les variables instrumentales appropriées, la méthode sous-espace permet d'estimer le modèle espace d'état pour la rigidité articulaire en boucles ouverte et fermée. Cette thèse présente également une méthode sous-espace pour identifier les modèles d'espace d'état pour les systèmes biomédicauxou les systèmes variant dans le temps caractérisés par des phénomènes transitoires de courte durée. Les simulations et les résultats expérimentaux démontrent que ces algorithmes fournissent des estimations précises en fonction de leurs conditions propres.
Chui, Nelson Loong Chik. "Subspace methods and informative experiments for system identification." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298794.
Dahlen, Anders. "Identification of stochastic systems : Subspace methods and covariance extension." Doctoral thesis, KTH, Mathematics, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3178.
Zhou, Ning. "Subspace methods of system identification applied to power systems." Laramie, Wyo. : University of Wyoming, 2005. http://proquest.umi.com/pqdweb?did=1095432761&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.
Dahlén, Anders. "Identification of stochastic systems : subspace methods and covariance extension /." Stockholm : Tekniska högsk, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3178.
Lam, Xuan-Binh. "Uncertainty quantification for stochastic subspace indentification methods." Rennes 1, 2011. http://www.theses.fr/2011REN1S133.
En analyse modale operationelle, les paramètres modaux (fréquence, amortissement, déforméees) peuvent être obtenus par des méthodes d'identification de type sous espaces et sont définis à une incertitude stochastique près. Pour évaluer la qualité des résultats obtenus, il est essentiel de connaître les bornes de confiance sur ces résultats. Dans cette thèse sont développés des algorithmes qui calcule automatiquement de telles bornes de confiance pour des paramètres modaux caractèristiques d'une structure mécanique. Ces algorithmes sont validés sur des exemples industriels significatifs. L'incertitude est tout d'abord calculé sur les données puis propagée sur les matrices du système par calcul de sensibilité, puis finalement sur les paramètres modaux. Les algorithmes existants sur lesquels se basent cette thèse dérivent l'incertitude des matrices du système de l'incertitude sur les covariances des entrées mesurées. Dans cette thèse, plusieurs résultats ont été obtenus. Tout d'abord, l'incertitude sur les déformées modales est obtenue par un schema de calcul plus réaliste que précédemment, utilisant une normalisation par l'angle de phase de la composante de valeur maximale. Ensuite, plusieurs méthodes de sous espaces et non seulement les méthodes à base de covariance sont considérées, telles que la méthode de réalisation stochastique ERA ainsi que la méthode UPC, à base des données. Pour ces méthodes, le calcul d'incertitude est explicité. Deu autres problèmatiques sont adressés : tout d'abord l'estimation multi ordre par méthode de sous espace et l'estimation à partir de jeux de données mesurées séparément. Pour ces deux problèmes, les schemas d'incertitude sont développés. En conclusion, cette thèse s'est attaché à développer des schemas de calcul d'incertitude pour une famille de méthodes sous espaces ainsi que pour un certain nombre de problèmes pratiques. La thèse finit avec le calcul d'incertitudes pour les méthodes récursives. Les méthodes sous espaces sont considérées comme une approche d'estimation robuste et consistante pour l'extraction des paramètres modaux à partir de données temporelles. Le calcul des incertitudes pour ces méthodes est maintenant possible, rendant ces méthodes encore plus crédible dans le cadre de l'exploitation de l'analyse modale
Nilsen, Geir Werner. "Topics in open and closed loop subspace system identification : finite data-based methods." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1752.
Ivanova, Elena. "Identification de systèmes multivariables par modèle non entier en utilisant la méthode des sous-espaces." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0561/document.
The identification of systems by fractional models was initiated in the 1990s and various results have been obtained since. Nevertheless, most of these results are based on prediction error methods (PEM) of identification, based on the minimization of the norm of the estimation error. Apparent in 1996, the subspace methods are relatively new in the theory of the identification of linear systems. Based on geometric projections and linear algebra, they present an alternative to classical methods based on linear or nonlinear regression. They allow estimating the matrices of the state-space representation of a system. In the context of fractional systems, a pseudo-state-space representation generalizes the notion of state-space representation by introducing an additional parameter which is the commensurable order.Currently, the subspace method for non-integer systems has only been applied inthe time domain. It is then developed in this thesis for such a class of systems in the frequency domain. Moreover, since non-integer systems are continuous time systems, datapre-filtering is necessary to respect the causality of the signals and to be able to realize the identification. A study of the different filtering methods in the context of subspaceidentification is then carried out in order to deduce their advantages and disadvantages in the time domain. Finally, the method has been applied to a thermal diffusion system.The obtained models are generalized for several input heat flows, considering their temperature available at several measurement points
Jorajuria, Corentin. "Estimation de l'amortissement des aubages en analyse modale opérationnelle." Electronic Thesis or Diss., Ecully, Ecole centrale de Lyon, 2024. http://www.theses.fr/2024ECDL0003.
European goals to reduce air traffic environmental impacts leads to design new civilian turbojet engines. These new designs can result in more severe aeroelastic risks for turbojet engines. In this regard, understanding and predicting dissipation phenomena is a key industrial challenge. As these phenomena can be very wide and complex, experimental approaches take an important role to understand damping. This thesis focuses on the estimation of damping of fan of civilian turbojet engines. To this end, estimation methods in frequency and time domain have been studied. The estimation issues are addressed thanks to a test rig making possible to measure vibratory responses of rotating full-scale fan in vacuum conditions using piezoelectric excitations. Moreover, subspace identification methods, showing particular advantages for the estimation of modes of rotating fans, have been investigated more specifically. Estimation performances of these techniques have been assessed over numerical models. Then, these techniques have been applied over vibratory measurements of a rotating fan in vacuum conditions. Furthermore, experimental data of fans in operation show that excitations can induce significant transient responses. Accordingly, an experimental study evaluating the effect of unsteady responses over modal characterization has been carried out. This experimental study has been performed thanks to modal tests using excitations with different unsteady rate. Finally, estimation methods showing encouraging results over modal tests of a rotating fan in vacuum conditions have been applied over experimental data obtained in operational conditions
Книги з теми "Subspace identification methods":
Katayama, Tohru. Subspace methods for system identification. London: Springer, 2005.
Katayama, Tohru. Subspace Methods for System Identification. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-158-x.
Katayama, Tohru. Subspace Methods for System Identification. Springer London, Limited, 2010.
Katayama, Tohru. Subspace Methods for System Identification. Springer London, Limited, 2006.
Частини книг з теми "Subspace identification methods":
Isermann, Rolf, and Marco Münchhof. "Subspace Methods." In Identification of Dynamic Systems, 409–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-78879-9_16.
Moonen, Marc, Bart Moor, and Joos Vandewalle. "SVD-based subspace methods for multivariable continuous-time systems identification." In Identification of Continuous-Time Systems, 473–88. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3558-0_15.
Katayama, Tohru. "Role of LQ Decomposition in Subspace Identification Methods." In Lecture Notes in Control and Information Sciences, 207–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73570-0_17.
Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Statistics Decomposition and Monitoring in Original Variable Space." In Intelligent Control and Learning Systems, 79–100. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_6.
Kim, Junhee, and Jerome P. Lynch. "Comparison Study of Output-only Subspace and Frequency-Domain Methods for System Identification of Base Excited Civil Engineering Structures." In Civil Engineering Topics, Volume 4, 305–12. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9316-8_28.
Mirzaei, M., J. W. Bredewout, and R. K. Snieder. "Gravity Data Inversion Using the Subspace Method." In Parameter Identification and Inverse Problems in Hydrology, Geology and Ecology, 187–98. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-1704-0_11.
Zhang, Zhenguo, Xiuchang Huang, Zhiyi Zhang, and Hongxing Hua. "Force Identification Based on Subspace Identification Algorithms and Homotopy Method." In Dynamics of Coupled Structures, Volume 4, 25–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29763-7_4.
Zhu, Rui, Stefano Marchesiello, Dario Anastasio, Dong Jiang, and Qingguo Fei. "Identification of Nonlinear Damping Using Nonlinear Subspace Method." In NODYCON Conference Proceedings Series, 369–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-81166-2_33.
Döhler, Michael, Palle Andersen, and Laurent Mevel. "Operational Modal Analysis Using a Fast Stochastic Subspace Identification Method." In Topics in Modal Analysis I, Volume 5, 19–24. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2425-3_3.
Iglesia, Daniel I., Carlos J. Escudero, and Luis Castedo. "A Subspace Method for Blind Channel Identification in Multi-Carrier CDMA Systems." In Multi-Carrier Spread Spectrum & Related Topics, 167–74. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4463-0_19.
Тези доповідей конференцій з теми "Subspace identification methods":
Jamaludin, I. W., N. A. Wahab, N. S. Khalid, S. Sahlan, Z. Ibrahim, and M. F. Rahmat. "N4SID and MOESP subspace identification methods." In 2013 IEEE 9th International Colloquium on Signal Processing & its Applications (CSPA). IEEE, 2013. http://dx.doi.org/10.1109/cspa.2013.6530030.
Tauchmanova, Jana, and Martin Hromcik. "Subspace identification methods and fMRI analysis." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4650193.
Shi, R., and J. F. MacGregor. "A framework for subspace identification methods." In Proceedings of American Control Conference. IEEE, 2001. http://dx.doi.org/10.1109/acc.2001.946206.
Chen, Huixin, Jan Maciejowski, and Chris Cox. "Unbiased bilinear subspace system identification methods." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076303.
Turkay, Semiha, and Huseyin Akcay. "Road profile modeling by subspace identification methods." In 2015 15th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2015. http://dx.doi.org/10.1109/iccas.2015.7364616.
Trnka, Pavel, and Vladimir Havlena. "Integrating Prior Information into Subspace Identification Methods." In 2007 IEEE International Conference on Control Applications. IEEE, 2007. http://dx.doi.org/10.1109/cca.2007.4389392.
Jamaludin, I. W., N. A. Wahab, M. F. Rahmat, and S. Sahlan. "Online subspace identification methods for MIMO model." In 2012 IEEE Conference on Control, Systems & Industrial Informatics (ICCSII). IEEE, 2012. http://dx.doi.org/10.1109/ccsii.2012.6470466.
Trnka, Pavel, and Vladimir Havlena. "Integrating Prior Information into Subspace Identification Methods." In 2007 IEEE 22nd International Symposium on Intelligent Control. IEEE, 2007. http://dx.doi.org/10.1109/isic.2007.4359771.
Nasir, Hasan Arshad, and Erik Weyer. "Comparison of prediction error methods and subspace identification methods for rivers." In 2013 3rd Australian Control Conference (AUCC). IEEE, 2013. http://dx.doi.org/10.1109/aucc.2013.6697309.
Lefkovits, Szidonia, and Laszlo Lefkovits. "Combining Subspace Methods and CNN Segmentation for Iris Identification." In 2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2019. http://dx.doi.org/10.1109/sami.2019.8782780.
Звіти організацій з теми "Subspace identification methods":
Nandanoori, Sai Pushpak, Kristine Arthur-Durett, Alejandro Heredia-Langner, and Thomas Edgar. A Data-driven approach to Determining the Fidelity in the Hardware-in-the-loop Systems using Subspace Identification Method. Office of Scientific and Technical Information (OSTI), February 2024. http://dx.doi.org/10.2172/2325016.