Literatura científica selecionada sobre o tema "Learning dynamical systems"
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Artigos de revistas sobre o assunto "Learning dynamical systems"
Hein, Helle, e Ulo Lepik. "LEARNING TRAJECTORIES OF DYNAMICAL SYSTEMS". Mathematical Modelling and Analysis 17, n.º 4 (1 de setembro de 2012): 519–31. http://dx.doi.org/10.3846/13926292.2012.706654.
Texto completo da fonteKhadivar, Farshad, Ilaria Lauzana e Aude Billard. "Learning dynamical systems with bifurcations". Robotics and Autonomous Systems 136 (fevereiro de 2021): 103700. http://dx.doi.org/10.1016/j.robot.2020.103700.
Texto completo da fonteBerry, Tyrus, e Suddhasattwa Das. "Learning Theory for Dynamical Systems". SIAM Journal on Applied Dynamical Systems 22, n.º 3 (8 de agosto de 2023): 2082–122. http://dx.doi.org/10.1137/22m1516865.
Texto completo da fonteRoy, Sayan, e Debanjan Rana. "Machine Learning in Nonlinear Dynamical Systems". Resonance 26, n.º 7 (julho de 2021): 953–70. http://dx.doi.org/10.1007/s12045-021-1194-0.
Texto completo da fonteWANG, CONG, TIANRUI CHEN, GUANRONG CHEN e DAVID J. HILL. "DETERMINISTIC LEARNING OF NONLINEAR DYNAMICAL SYSTEMS". International Journal of Bifurcation and Chaos 19, n.º 04 (abril de 2009): 1307–28. http://dx.doi.org/10.1142/s0218127409023640.
Texto completo da fonteAhmadi, Amir Ali, e Bachir El Khadir. "Learning Dynamical Systems with Side Information". SIAM Review 65, n.º 1 (fevereiro de 2023): 183–223. http://dx.doi.org/10.1137/20m1388644.
Texto completo da fonteGrigoryeva, Lyudmila, Allen Hart e Juan-Pablo Ortega. "Learning strange attractors with reservoir systems". Nonlinearity 36, n.º 9 (27 de julho de 2023): 4674–708. http://dx.doi.org/10.1088/1361-6544/ace492.
Texto completo da fonteDavids, Keith. "Learning design for Nonlinear Dynamical Movement Systems". Open Sports Sciences Journal 5, n.º 1 (13 de setembro de 2012): 9–16. http://dx.doi.org/10.2174/1875399x01205010009.
Texto completo da fonteCampi, M. C., e P. R. Kumar. "Learning dynamical systems in a stationary environment". Systems & Control Letters 34, n.º 3 (junho de 1998): 125–32. http://dx.doi.org/10.1016/s0167-6911(98)00005-x.
Texto completo da fonteRajendra, P., e V. Brahmajirao. "Modeling of dynamical systems through deep learning". Biophysical Reviews 12, n.º 6 (22 de novembro de 2020): 1311–20. http://dx.doi.org/10.1007/s12551-020-00776-4.
Texto completo da fonteTeses / dissertações sobre o assunto "Learning dynamical systems"
Preen, Richard John. "Dynamical genetic programming in learning classifier systems". Thesis, University of the West of England, Bristol, 2011. http://eprints.uwe.ac.uk/25852/.
Texto completo da fonteFerizbegovic, Mina. "Robust learning and control of linear dynamical systems". Licentiate thesis, KTH, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280121.
Texto completo da fonteQC 20200904
Mazzoleni, Mirko (ORCID:0000-0002-7116-135X). "Learning meets control. Data analytics for dynamical systems". Doctoral thesis, Università degli studi di Bergamo, 2018. http://hdl.handle.net/10446/104812.
Texto completo da fonteIzquierdo, Eduardo J. "The dynamics of learning behaviour : a situated, embodied, and dynamical systems approach". Thesis, University of Sussex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488595.
Texto completo da fonteMussmann, Thomas Frederick. "Data Driven Learning of Dynamical Systems Using Neural Networks". The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618589877977348.
Texto completo da fonteLindsten, Fredrik. "Particle filters and Markov chains for learning of dynamical systems". Doctoral thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97692.
Texto completo da fonteCNDM
CADICS
Mao, Weize. "DATA-DRIVEN LEARNING OF UNKNOWN DYNAMICAL SYSTEMS WITH MISSING INFORMATION". The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619097149112362.
Texto completo da fontePassey, Jr David Joseph. "Growing Complex Networks for Better Learning of Chaotic Dynamical Systems". BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8146.
Texto completo da fonteBézenac, Emmanuel de. "Modeling physical processes with deep learning : a dynamical systems approach". Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS203.
Texto completo da fonteDeep Learning has emerged as a predominant tool for AI, and has already abundant applications in fields where data is abundant and access to prior knowledge is difficult. This is not necessarily the case for natural sciences, and in particular, for physical processes. Indeed, these have been the object of study since centuries, a vast amount of knowledge has been acquired, and elaborate algorithms and methods have been developped. Thus, this thesis has two main objectives. The first considers the study of the role that deep learning has to play in this vast ecosystem of knowledge, theory and tools. We will attempt to answer this general question through a concrete problem: the one of modelling complex physical processes, leveraging deep learning methods in order to make up for lacking prior knowledge. The second objective is somewhat its converse: it focuses on how perspectives, insights and tools from the field of study of physical processes and dynamical systems can be applied in the context of deep learning, in order to gain a better understanding and develop novel algorithms
Appeltant, Lennert. "Reservoir computing based on delay-dynamical systems". Doctoral thesis, Universitat de les Illes Balears, 2012. http://hdl.handle.net/10803/84144.
Texto completo da fonteLivros sobre o assunto "Learning dynamical systems"
P, Spencer John, Thomas Michael S. C e McClelland James L, eds. Toward a unified theory of development: Connectionism and dynamic systems theory re-considered. Oxford: Oxford University Press, 2009.
Encontre o texto completo da fonteRussell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.
Encontre o texto completo da fonteHaridimos, Tsoukas, e Mylonopoulos Nikolaos 1970-, eds. Organizations as knowledge systems: Knowledge, learning, and dynamic capabilities. New York: Palgrave Macmillan, 2004.
Encontre o texto completo da fonteservice), SpringerLink (Online, ed. Self-Evolvable Systems: Machine Learning in Social Media. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Encontre o texto completo da fonteGroup model building: Facilitating team learning using system dynamics. Chichester: J. Wiley, 1996.
Encontre o texto completo da fonteAldo, Romano, e SpringerLink (Online service), eds. Dynamic Learning Networks: Models and Cases in Action. Boston, MA: Springer-Verlag US, 2009.
Encontre o texto completo da fonteHiroyuki, Itami. Dynamics of Knowledge, Corporate Systems and Innovation. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.
Encontre o texto completo da fonteReinforcement learning and dynamic programming using function approximators. Boca Raton: CRC Press, 2010.
Encontre o texto completo da fonteSandrock, Jörg. System dynamics in der strategischen Planung: Zur Gestaltung von Geschäftsmodellen im E-Learning. Wiesbaden: Dt. Univ.-Verl., 2006.
Encontre o texto completo da fonteIEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu, Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Learning dynamical systems"
Andonov, Sasho. "Non-linear Dynamical Systems". In Learning and Relearning Equipment Complexity, 121–61. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003404811-9.
Texto completo da fonteTanaka, Akinori, Akio Tomiya e Koji Hashimoto. "Dynamical Systems and Neural Networks". In Deep Learning and Physics, 147–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6108-9_9.
Texto completo da fonteGolden, Richard M. "Convergence of Time-Invariant Dynamical Systems". In Statistical Machine Learning, 169–86. First edition. j Boca Raton, FL : CRC Press, 2020. j Includes bibliographical references and index.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781351051507-6.
Texto completo da fonteGros, Claudius. "Complexity of Machine Learning". In Complex and Adaptive Dynamical Systems, 361–92. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55076-8_10.
Texto completo da fonteBhat, Harish S., e Shagun Rawat. "Learning Stochastic Dynamical Systems via Bridge Sampling". In Advanced Analytics and Learning on Temporal Data, 183–98. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39098-3_14.
Texto completo da fonteGaucel, Sébastien, Maarten Keijzer, Evelyne Lutton e Alberto Tonda. "Learning Dynamical Systems Using Standard Symbolic Regression". In Lecture Notes in Computer Science, 25–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44303-3_3.
Texto completo da fonteVidyasagar, M. "Learning, System IdentificationSystem identification , and Complexity". In Mathematics of Complexity and Dynamical Systems, 924–36. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1806-1_55.
Texto completo da fonteStamovlasis, Dimitrios. "Catastrophe Theory: Methodology, Epistemology, and Applications in Learning Science". In Complex Dynamical Systems in Education, 141–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27577-2_9.
Texto completo da fonteLagos, Guido, e Pablo Romero. "On the Reliability of Dynamical Stochastic Binary Systems". In Machine Learning, Optimization, and Data Science, 516–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_46.
Texto completo da fonteLiu, Xuanwu, Zhao Li, Yuanhui Mao, Lixiang Lai, Ben Gao, Yao Deng e Guoxian Yu. "Dynamical User Intention Prediction via Multi-modal Learning". In Database Systems for Advanced Applications, 519–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59410-7_35.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Learning dynamical systems"
Sommer, Nicolas, Klas Kronander e Aude Billard. "Learning externally modulated dynamical systems". In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017. http://dx.doi.org/10.1109/iros.2017.8206248.
Texto completo da fonteHudson, Joshua, Khachik Sargsyan, Marta D'Elia e Habib Najm. "Analysis of Neural Networks as Dynamical Systems." In Proposed for presentation at the Sandia Machine Learning and Deep Learning Workshop in ,. US DOE, 2021. http://dx.doi.org/10.2172/1883507.
Texto completo da fonteLiu, G. P. "Neural-learning control of nonlinear dynamical systems". In IEE Seminar Learning Systems for Control. IEE, 2000. http://dx.doi.org/10.1049/ic:20000346.
Texto completo da fonteArimoto, S., S. Kawamura, F. Miyazaki e S. Tamaki. "Learning control theory for dynamical systems". In 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268737.
Texto completo da fonteKrause, Andre Frank, Volker Durr, Thomas Schack e Holk Cruse. "Input compensation learning: Modelling dynamical systems". In 2011 Seventh International Conference on Natural Computation (ICNC). IEEE, 2011. http://dx.doi.org/10.1109/icnc.2011.6022106.
Texto completo da fonteCong Wang, D. J. Hill e Guanrong Chen. "Deterministic learning of nonlinear dynamical systems". In Proceedings of the 2003 IEEE International Symposium on Intelligent Control. IEEE, 2003. http://dx.doi.org/10.1109/isic.2003.1253919.
Texto completo da fonteLiu, Ren-Huiliu, e Xue-Feng Lv. "Control for Switched Systems using Output Dynamical Compensator". In Sixth International Conference on Machine Learning Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370215.
Texto completo da fonteQuintanilla, Rafael, e John T. Wen. "Iterative learning control for nonsmooth dynamical systems". In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434885.
Texto completo da fonteHuang, Tzu-Kuo, e Jeff Schneider. "Learning linear dynamical systems without sequence information". In the 26th Annual International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553430.
Texto completo da fonteSaveriano, Matteo, e Dongheui Lee. "Incremental Skill Learning of Stable Dynamical Systems". In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8594474.
Texto completo da fonteRelatórios de organizações sobre o assunto "Learning dynamical systems"
Siddiqi, Sajid M., Byron Boots e Geoffrey J. Gordon. A Constraint Generation Approach to Learning Stable Linear Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 2008. http://dx.doi.org/10.21236/ada480921.
Texto completo da fonteRupe, Adam. Learning Implicit Models of Complex Dynamical Systems From Partial Observations. Office of Scientific and Technical Information (OSTI), julho de 2021. http://dx.doi.org/10.2172/1808822.
Texto completo da fonteKaffenberger, Michelle, e Marla Spivack. System Coherence for Learning: Applications of the RISE Education Systems Framework. Research on Improving Systems of Education (RISE), janeiro de 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/086.
Texto completo da fonteJiang, Zhong-Ping. Cognitive Models for Learning to Control Dynamic Systems. Fort Belvoir, VA: Defense Technical Information Center, setembro de 2008. http://dx.doi.org/10.21236/ada487160.
Texto completo da fonteKaffenberger, Michelle, Jason Silberstein e Marla Spivack. Evaluating Systems: Three Approaches for Analyzing Education Systems and Informing Action. Research on Improving Systems of Education (RISE), abril de 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/093.
Texto completo da fonteDeppe, Sahar. AI-based reccomendation system for industrial training. Kompetenzzentrum Arbeitswelt.Plus, dezembro de 2023. http://dx.doi.org/10.55594/vmtx7119.
Texto completo da fonteRoss-Larson, Bruce. Why Students Aren’t Learning What They Need for a Productive Life. Research on Improving Systems of Education (RISE), março de 2023. http://dx.doi.org/10.35489/bsg-rise-2023/pe13.
Texto completo da fonteWilinski, Mateusz. Learning of the full dynamic system state matrix from partial PMU observations. Office of Scientific and Technical Information (OSTI), março de 2022. http://dx.doi.org/10.2172/1853893.
Texto completo da fonteYuan, Yuxuan, Zhaoyu Wang, Ian Dobson, Venkataramana Ajjarapu, Jie Chen e Neeraj Nayak. Robust Learning of Dynamic Interactions for Enhancing Power System Resilience Final Scientific Report. Office of Scientific and Technical Information (OSTI), março de 2022. http://dx.doi.org/10.2172/1878168.
Texto completo da fonteOsypova, Nataliia V., e Volodimir I. Tatochenko. Improving the learning environment for future mathematics teachers with the use application of the dynamic mathematics system GeoGebra AR. [б. в.], julho de 2021. http://dx.doi.org/10.31812/123456789/4628.
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