Literatura científica selecionada sobre o tema "Subspace identification methods"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Índice
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Subspace identification methods".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Subspace identification methods"
Dalen, Christer, e David Di Ruscio. "On subspace system identification methods". Modeling, Identification and Control: A Norwegian Research Bulletin 43, n.º 4 (2022): 119–30. http://dx.doi.org/10.4173/mic.2022.4.1.
Texto completo da fonteViberg, Mats. "Subspace Methods in System Identification". IFAC Proceedings Volumes 27, n.º 8 (julho de 1994): 1–12. http://dx.doi.org/10.1016/s1474-6670(17)47689-0.
Texto completo da fonteJoe Qin, S. "Subspace methods for system identification". Automatica 43, n.º 4 (abril de 2007): 748–49. http://dx.doi.org/10.1016/j.automatica.2006.07.027.
Texto completo da fonteAvcıoğlu, Sevil, Ali Türker Kutay e Kemal Leblebicioğlu. "Identification of Physical Helicopter Models Using Subspace Identification". Journal of the American Helicopter Society 65, n.º 2 (1 de abril de 2020): 1–14. http://dx.doi.org/10.4050/jahs.65.022001.
Texto completo da fonteMiller, Daniel N., e Raymond A. de Callafon. "Subspace Identification From Classical Realization Methods". IFAC Proceedings Volumes 42, n.º 10 (2009): 102–7. http://dx.doi.org/10.3182/20090706-3-fr-2004.00016.
Texto completo da fonteMathieu, Pouliquen, e M'Saad Mohammed. "AN INTERPRETATION OF SUBSPACE IDENTIFICATION METHODS". IFAC Proceedings Volumes 38, n.º 1 (2005): 904–9. http://dx.doi.org/10.3182/20050703-6-cz-1902.00152.
Texto completo da fonteWani Jamaludin, Irma Wani Jamaludin, e Norhaliza Abdul Wahab. "Recursive Subspace Identification Algorithm using the Propagator Based Method". Indonesian Journal of Electrical Engineering and Computer Science 6, n.º 1 (1 de abril de 2017): 172. http://dx.doi.org/10.11591/ijeecs.v6.i1.pp172-179.
Texto completo da fonteMohd-Mokhtar, Rosmiwati, e Liuping Wang. "Continuous time system identification using subspace methods". ANZIAM Journal 48 (26 de junho de 2007): 712. http://dx.doi.org/10.21914/anziamj.v47i0.1072.
Texto completo da fonteMuradore, Riccardo, e Enrico Fedrigo. "SUBSPACE IDENTIFICATION METHODS APPLIED TO ADAPTIVE OPTICS". IFAC Proceedings Volumes 39, n.º 1 (2006): 943–48. http://dx.doi.org/10.3182/20060329-3-au-2901.00150.
Texto completo da fontevan der Veen, Gijs, Jan-Willem van Wingerden, Marco Lovera, Marco Bergamasco e Michel Verhaegen. "Closed-loop subspace identification methods: an overview". IET Control Theory & Applications 7, n.º 10 (4 de julho de 2013): 1339–58. http://dx.doi.org/10.1049/iet-cta.2012.0653.
Texto completo da fonteTeses / dissertações sobre o assunto "Subspace identification methods"
Shi, Ruijie. "Subspace identification methods for process dynamic modeling /". *McMaster only, 2001.
Encontre o texto completo da fonteZhao, 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.
Texto completo da fonteL'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.
Texto completo da fonteDahlen, 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.
Texto completo da fonteZhou, 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.
Texto completo da fonteDahlén, 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.
Texto completo da fonteLam, Xuan-Binh. "Uncertainty quantification for stochastic subspace indentification methods". Rennes 1, 2011. http://www.theses.fr/2011REN1S133.
Texto completo da fonteEn 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.
Texto completo da fonteIvanova, 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.
Texto completo da fonteThe 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.
Texto completo da fonteEuropean 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
Livros sobre o assunto "Subspace identification methods"
Katayama, Tohru. Subspace methods for system identification. London: Springer, 2005.
Encontre o texto completo da fonteKatayama, Tohru. Subspace Methods for System Identification. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-158-x.
Texto completo da fonteKatayama, Tohru. Subspace Methods for System Identification. Springer London, Limited, 2010.
Encontre o texto completo da fonteKatayama, Tohru. Subspace Methods for System Identification. Springer London, Limited, 2006.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Subspace identification methods"
Isermann, Rolf, e 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.
Texto completo da fonteMoonen, Marc, Bart Moor e 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.
Texto completo da fonteKatayama, 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.
Texto completo da fonteWang, Jing, Jinglin Zhou e 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.
Texto completo da fonteKim, Junhee, e 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.
Texto completo da fonteMirzaei, M., J. W. Bredewout e 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.
Texto completo da fonteZhang, Zhenguo, Xiuchang Huang, Zhiyi Zhang e 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.
Texto completo da fonteZhu, Rui, Stefano Marchesiello, Dario Anastasio, Dong Jiang e 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.
Texto completo da fonteDöhler, Michael, Palle Andersen e 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.
Texto completo da fonteIglesia, Daniel I., Carlos J. Escudero e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Subspace identification methods"
Jamaludin, I. W., N. A. Wahab, N. S. Khalid, S. Sahlan, Z. Ibrahim e 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.
Texto completo da fonteTauchmanova, Jana, e 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.
Texto completo da fonteShi, R., e 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.
Texto completo da fonteChen, Huixin, Jan Maciejowski e 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.
Texto completo da fonteTurkay, Semiha, e 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.
Texto completo da fonteTrnka, Pavel, e 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.
Texto completo da fonteJamaludin, I. W., N. A. Wahab, M. F. Rahmat e 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.
Texto completo da fonteTrnka, Pavel, e 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.
Texto completo da fonteNasir, Hasan Arshad, e 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.
Texto completo da fonteLefkovits, Szidonia, e 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "Subspace identification methods"
Nandanoori, Sai Pushpak, Kristine Arthur-Durett, Alejandro Heredia-Langner e 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), fevereiro de 2024. http://dx.doi.org/10.2172/2325016.
Texto completo da fonte