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Artykuły w czasopismach na temat "Learning dynamical systems"
Hein, Helle, i Ulo Lepik. "LEARNING TRAJECTORIES OF DYNAMICAL SYSTEMS". Mathematical Modelling and Analysis 17, nr 4 (1.09.2012): 519–31. http://dx.doi.org/10.3846/13926292.2012.706654.
Pełny tekst źródłaKhadivar, Farshad, Ilaria Lauzana i Aude Billard. "Learning dynamical systems with bifurcations". Robotics and Autonomous Systems 136 (luty 2021): 103700. http://dx.doi.org/10.1016/j.robot.2020.103700.
Pełny tekst źródłaBerry, Tyrus, i Suddhasattwa Das. "Learning Theory for Dynamical Systems". SIAM Journal on Applied Dynamical Systems 22, nr 3 (8.08.2023): 2082–122. http://dx.doi.org/10.1137/22m1516865.
Pełny tekst źródłaRoy, Sayan, i Debanjan Rana. "Machine Learning in Nonlinear Dynamical Systems". Resonance 26, nr 7 (lipiec 2021): 953–70. http://dx.doi.org/10.1007/s12045-021-1194-0.
Pełny tekst źródłaWANG, CONG, TIANRUI CHEN, GUANRONG CHEN i DAVID J. HILL. "DETERMINISTIC LEARNING OF NONLINEAR DYNAMICAL SYSTEMS". International Journal of Bifurcation and Chaos 19, nr 04 (kwiecień 2009): 1307–28. http://dx.doi.org/10.1142/s0218127409023640.
Pełny tekst źródłaAhmadi, Amir Ali, i Bachir El Khadir. "Learning Dynamical Systems with Side Information". SIAM Review 65, nr 1 (luty 2023): 183–223. http://dx.doi.org/10.1137/20m1388644.
Pełny tekst źródłaGrigoryeva, Lyudmila, Allen Hart i Juan-Pablo Ortega. "Learning strange attractors with reservoir systems". Nonlinearity 36, nr 9 (27.07.2023): 4674–708. http://dx.doi.org/10.1088/1361-6544/ace492.
Pełny tekst źródłaDavids, Keith. "Learning design for Nonlinear Dynamical Movement Systems". Open Sports Sciences Journal 5, nr 1 (13.09.2012): 9–16. http://dx.doi.org/10.2174/1875399x01205010009.
Pełny tekst źródłaCampi, M. C., i P. R. Kumar. "Learning dynamical systems in a stationary environment". Systems & Control Letters 34, nr 3 (czerwiec 1998): 125–32. http://dx.doi.org/10.1016/s0167-6911(98)00005-x.
Pełny tekst źródłaRajendra, P., i V. Brahmajirao. "Modeling of dynamical systems through deep learning". Biophysical Reviews 12, nr 6 (22.11.2020): 1311–20. http://dx.doi.org/10.1007/s12551-020-00776-4.
Pełny tekst źródłaRozprawy doktorskie na temat "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/.
Pełny tekst źródłaFerizbegovic, 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.
Pełny tekst źródłaQC 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.
Pełny tekst źródłaIzquierdo, 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.
Pełny tekst źródłaMussmann, 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.
Pełny tekst źródłaLindsten, 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.
Pełny tekst źródłaCNDM
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.
Pełny tekst źródłaPassey, Jr David Joseph. "Growing Complex Networks for Better Learning of Chaotic Dynamical Systems". BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8146.
Pełny tekst źródłaBézenac, Emmanuel de. "Modeling physical processes with deep learning : a dynamical systems approach". Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS203.
Pełny tekst źródłaDeep 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.
Pełny tekst źródłaKsiążki na temat "Learning dynamical systems"
P, Spencer John, Thomas Michael S. C i McClelland James L, red. Toward a unified theory of development: Connectionism and dynamic systems theory re-considered. Oxford: Oxford University Press, 2009.
Znajdź pełny tekst źródłaRussell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.
Znajdź pełny tekst źródłaHaridimos, Tsoukas, i Mylonopoulos Nikolaos 1970-, red. Organizations as knowledge systems: Knowledge, learning, and dynamic capabilities. New York: Palgrave Macmillan, 2004.
Znajdź pełny tekst źródłaservice), SpringerLink (Online, red. Self-Evolvable Systems: Machine Learning in Social Media. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Znajdź pełny tekst źródłaGroup model building: Facilitating team learning using system dynamics. Chichester: J. Wiley, 1996.
Znajdź pełny tekst źródłaAldo, Romano, i SpringerLink (Online service), red. Dynamic Learning Networks: Models and Cases in Action. Boston, MA: Springer-Verlag US, 2009.
Znajdź pełny tekst źródłaHiroyuki, Itami. Dynamics of Knowledge, Corporate Systems and Innovation. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.
Znajdź pełny tekst źródłaReinforcement learning and dynamic programming using function approximators. Boca Raton: CRC Press, 2010.
Znajdź pełny tekst źródłaSandrock, Jörg. System dynamics in der strategischen Planung: Zur Gestaltung von Geschäftsmodellen im E-Learning. Wiesbaden: Dt. Univ.-Verl., 2006.
Znajdź pełny tekst źródłaIEEE 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.
Znajdź pełny tekst źródłaCzęści książek na temat "Learning dynamical systems"
Andonov, Sasho. "Non-linear Dynamical Systems". W Learning and Relearning Equipment Complexity, 121–61. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003404811-9.
Pełny tekst źródłaTanaka, Akinori, Akio Tomiya i Koji Hashimoto. "Dynamical Systems and Neural Networks". W Deep Learning and Physics, 147–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6108-9_9.
Pełny tekst źródłaGolden, Richard M. "Convergence of Time-Invariant Dynamical Systems". W 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.
Pełny tekst źródłaGros, Claudius. "Complexity of Machine Learning". W Complex and Adaptive Dynamical Systems, 361–92. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55076-8_10.
Pełny tekst źródłaBhat, Harish S., i Shagun Rawat. "Learning Stochastic Dynamical Systems via Bridge Sampling". W 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.
Pełny tekst źródłaGaucel, Sébastien, Maarten Keijzer, Evelyne Lutton i Alberto Tonda. "Learning Dynamical Systems Using Standard Symbolic Regression". W 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.
Pełny tekst źródłaVidyasagar, M. "Learning, System IdentificationSystem identification , and Complexity". W 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.
Pełny tekst źródłaStamovlasis, Dimitrios. "Catastrophe Theory: Methodology, Epistemology, and Applications in Learning Science". W Complex Dynamical Systems in Education, 141–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27577-2_9.
Pełny tekst źródłaLagos, Guido, i Pablo Romero. "On the Reliability of Dynamical Stochastic Binary Systems". W 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.
Pełny tekst źródłaLiu, Xuanwu, Zhao Li, Yuanhui Mao, Lixiang Lai, Ben Gao, Yao Deng i Guoxian Yu. "Dynamical User Intention Prediction via Multi-modal Learning". W Database Systems for Advanced Applications, 519–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59410-7_35.
Pełny tekst źródłaStreszczenia konferencji na temat "Learning dynamical systems"
Sommer, Nicolas, Klas Kronander i Aude Billard. "Learning externally modulated dynamical systems". W 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017. http://dx.doi.org/10.1109/iros.2017.8206248.
Pełny tekst źródłaHudson, Joshua, Khachik Sargsyan, Marta D'Elia i Habib Najm. "Analysis of Neural Networks as Dynamical Systems." W Proposed for presentation at the Sandia Machine Learning and Deep Learning Workshop in ,. US DOE, 2021. http://dx.doi.org/10.2172/1883507.
Pełny tekst źródłaLiu, G. P. "Neural-learning control of nonlinear dynamical systems". W IEE Seminar Learning Systems for Control. IEE, 2000. http://dx.doi.org/10.1049/ic:20000346.
Pełny tekst źródłaArimoto, S., S. Kawamura, F. Miyazaki i S. Tamaki. "Learning control theory for dynamical systems". W 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268737.
Pełny tekst źródłaKrause, Andre Frank, Volker Durr, Thomas Schack i Holk Cruse. "Input compensation learning: Modelling dynamical systems". W 2011 Seventh International Conference on Natural Computation (ICNC). IEEE, 2011. http://dx.doi.org/10.1109/icnc.2011.6022106.
Pełny tekst źródłaCong Wang, D. J. Hill i Guanrong Chen. "Deterministic learning of nonlinear dynamical systems". W Proceedings of the 2003 IEEE International Symposium on Intelligent Control. IEEE, 2003. http://dx.doi.org/10.1109/isic.2003.1253919.
Pełny tekst źródłaLiu, Ren-Huiliu, i Xue-Feng Lv. "Control for Switched Systems using Output Dynamical Compensator". W Sixth International Conference on Machine Learning Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370215.
Pełny tekst źródłaQuintanilla, Rafael, i John T. Wen. "Iterative learning control for nonsmooth dynamical systems". W 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434885.
Pełny tekst źródłaHuang, Tzu-Kuo, i Jeff Schneider. "Learning linear dynamical systems without sequence information". W the 26th Annual International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553430.
Pełny tekst źródłaSaveriano, Matteo, i Dongheui Lee. "Incremental Skill Learning of Stable Dynamical Systems". W 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8594474.
Pełny tekst źródłaRaporty organizacyjne na temat "Learning dynamical systems"
Siddiqi, Sajid M., Byron Boots i Geoffrey J. Gordon. A Constraint Generation Approach to Learning Stable Linear Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2008. http://dx.doi.org/10.21236/ada480921.
Pełny tekst źródłaRupe, Adam. Learning Implicit Models of Complex Dynamical Systems From Partial Observations. Office of Scientific and Technical Information (OSTI), lipiec 2021. http://dx.doi.org/10.2172/1808822.
Pełny tekst źródłaKaffenberger, Michelle, i Marla Spivack. System Coherence for Learning: Applications of the RISE Education Systems Framework. Research on Improving Systems of Education (RISE), styczeń 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/086.
Pełny tekst źródłaJiang, Zhong-Ping. Cognitive Models for Learning to Control Dynamic Systems. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2008. http://dx.doi.org/10.21236/ada487160.
Pełny tekst źródłaKaffenberger, Michelle, Jason Silberstein i Marla Spivack. Evaluating Systems: Three Approaches for Analyzing Education Systems and Informing Action. Research on Improving Systems of Education (RISE), kwiecień 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/093.
Pełny tekst źródłaDeppe, Sahar. AI-based reccomendation system for industrial training. Kompetenzzentrum Arbeitswelt.Plus, grudzień 2023. http://dx.doi.org/10.55594/vmtx7119.
Pełny tekst źródłaRoss-Larson, Bruce. Why Students Aren’t Learning What They Need for a Productive Life. Research on Improving Systems of Education (RISE), marzec 2023. http://dx.doi.org/10.35489/bsg-rise-2023/pe13.
Pełny tekst źródłaWilinski, Mateusz. Learning of the full dynamic system state matrix from partial PMU observations. Office of Scientific and Technical Information (OSTI), marzec 2022. http://dx.doi.org/10.2172/1853893.
Pełny tekst źródłaYuan, Yuxuan, Zhaoyu Wang, Ian Dobson, Venkataramana Ajjarapu, Jie Chen i Neeraj Nayak. Robust Learning of Dynamic Interactions for Enhancing Power System Resilience Final Scientific Report. Office of Scientific and Technical Information (OSTI), marzec 2022. http://dx.doi.org/10.2172/1878168.
Pełny tekst źródłaOsypova, Nataliia V., i Volodimir I. Tatochenko. Improving the learning environment for future mathematics teachers with the use application of the dynamic mathematics system GeoGebra AR. [б. в.], lipiec 2021. http://dx.doi.org/10.31812/123456789/4628.
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