Auswahl der wissenschaftlichen Literatur zum Thema „Learning dynamical systems“
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Zeitschriftenartikel zum Thema "Learning dynamical systems"
Hein, Helle, und Ulo Lepik. „LEARNING TRAJECTORIES OF DYNAMICAL SYSTEMS“. Mathematical Modelling and Analysis 17, Nr. 4 (01.09.2012): 519–31. http://dx.doi.org/10.3846/13926292.2012.706654.
Der volle Inhalt der QuelleKhadivar, Farshad, Ilaria Lauzana und Aude Billard. „Learning dynamical systems with bifurcations“. Robotics and Autonomous Systems 136 (Februar 2021): 103700. http://dx.doi.org/10.1016/j.robot.2020.103700.
Der volle Inhalt der QuelleBerry, Tyrus, und Suddhasattwa Das. „Learning Theory for Dynamical Systems“. SIAM Journal on Applied Dynamical Systems 22, Nr. 3 (08.08.2023): 2082–122. http://dx.doi.org/10.1137/22m1516865.
Der volle Inhalt der QuelleRoy, Sayan, und Debanjan Rana. „Machine Learning in Nonlinear Dynamical Systems“. Resonance 26, Nr. 7 (Juli 2021): 953–70. http://dx.doi.org/10.1007/s12045-021-1194-0.
Der volle Inhalt der QuelleWANG, CONG, TIANRUI CHEN, GUANRONG CHEN und DAVID J. HILL. „DETERMINISTIC LEARNING OF NONLINEAR DYNAMICAL SYSTEMS“. International Journal of Bifurcation and Chaos 19, Nr. 04 (April 2009): 1307–28. http://dx.doi.org/10.1142/s0218127409023640.
Der volle Inhalt der QuelleAhmadi, Amir Ali, und Bachir El Khadir. „Learning Dynamical Systems with Side Information“. SIAM Review 65, Nr. 1 (Februar 2023): 183–223. http://dx.doi.org/10.1137/20m1388644.
Der volle Inhalt der QuelleGrigoryeva, Lyudmila, Allen Hart und 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.
Der volle Inhalt der QuelleDavids, 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.
Der volle Inhalt der QuelleCampi, M. C., und P. R. Kumar. „Learning dynamical systems in a stationary environment“. Systems & Control Letters 34, Nr. 3 (Juni 1998): 125–32. http://dx.doi.org/10.1016/s0167-6911(98)00005-x.
Der volle Inhalt der QuelleRajendra, P., und 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.
Der volle Inhalt der QuelleDissertationen zum Thema "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/.
Der volle Inhalt der QuelleFerizbegovic, 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.
Der volle Inhalt der QuelleQC 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.
Der volle Inhalt der QuelleIzquierdo, 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.
Der volle Inhalt der QuelleMussmann, 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.
Der volle Inhalt der QuelleLindsten, 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.
Der volle Inhalt der QuelleCNDM
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.
Der volle Inhalt der QuellePassey, Jr David Joseph. „Growing Complex Networks for Better Learning of Chaotic Dynamical Systems“. BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8146.
Der volle Inhalt der QuelleBézenac, Emmanuel de. „Modeling physical processes with deep learning : a dynamical systems approach“. Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS203.
Der volle Inhalt der QuelleDeep 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.
Der volle Inhalt der QuelleBücher zum Thema "Learning dynamical systems"
P, Spencer John, Thomas Michael S. C und McClelland James L, Hrsg. Toward a unified theory of development: Connectionism and dynamic systems theory re-considered. Oxford: Oxford University Press, 2009.
Den vollen Inhalt der Quelle findenRussell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.
Den vollen Inhalt der Quelle findenHaridimos, Tsoukas, und Mylonopoulos Nikolaos 1970-, Hrsg. Organizations as knowledge systems: Knowledge, learning, and dynamic capabilities. New York: Palgrave Macmillan, 2004.
Den vollen Inhalt der Quelle findenservice), SpringerLink (Online, Hrsg. Self-Evolvable Systems: Machine Learning in Social Media. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Den vollen Inhalt der Quelle findenGroup model building: Facilitating team learning using system dynamics. Chichester: J. Wiley, 1996.
Den vollen Inhalt der Quelle findenAldo, Romano, und SpringerLink (Online service), Hrsg. Dynamic Learning Networks: Models and Cases in Action. Boston, MA: Springer-Verlag US, 2009.
Den vollen Inhalt der Quelle findenHiroyuki, Itami. Dynamics of Knowledge, Corporate Systems and Innovation. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.
Den vollen Inhalt der Quelle findenReinforcement learning and dynamic programming using function approximators. Boca Raton: CRC Press, 2010.
Den vollen Inhalt der Quelle findenSandrock, Jörg. System dynamics in der strategischen Planung: Zur Gestaltung von Geschäftsmodellen im E-Learning. Wiesbaden: Dt. Univ.-Verl., 2006.
Den vollen Inhalt der Quelle findenIEEE 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.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "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.
Der volle Inhalt der QuelleTanaka, Akinori, Akio Tomiya und 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.
Der volle Inhalt der QuelleGolden, 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.
Der volle Inhalt der QuelleGros, 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.
Der volle Inhalt der QuelleBhat, Harish S., und 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.
Der volle Inhalt der QuelleGaucel, Sébastien, Maarten Keijzer, Evelyne Lutton und 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.
Der volle Inhalt der QuelleVidyasagar, 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.
Der volle Inhalt der QuelleStamovlasis, 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.
Der volle Inhalt der QuelleLagos, Guido, und 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.
Der volle Inhalt der QuelleLiu, Xuanwu, Zhao Li, Yuanhui Mao, Lixiang Lai, Ben Gao, Yao Deng und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Learning dynamical systems"
Sommer, Nicolas, Klas Kronander und 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.
Der volle Inhalt der QuelleHudson, Joshua, Khachik Sargsyan, Marta D'Elia und 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.
Der volle Inhalt der QuelleLiu, 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.
Der volle Inhalt der QuelleArimoto, S., S. Kawamura, F. Miyazaki und 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.
Der volle Inhalt der QuelleKrause, Andre Frank, Volker Durr, Thomas Schack und 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.
Der volle Inhalt der QuelleCong Wang, D. J. Hill und 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.
Der volle Inhalt der QuelleLiu, Ren-Huiliu, und 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.
Der volle Inhalt der QuelleQuintanilla, Rafael, und 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.
Der volle Inhalt der QuelleHuang, Tzu-Kuo, und 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.
Der volle Inhalt der QuelleSaveriano, Matteo, und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Learning dynamical systems"
Siddiqi, Sajid M., Byron Boots und Geoffrey J. Gordon. A Constraint Generation Approach to Learning Stable Linear Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, Januar 2008. http://dx.doi.org/10.21236/ada480921.
Der volle Inhalt der QuelleRupe, Adam. Learning Implicit Models of Complex Dynamical Systems From Partial Observations. Office of Scientific and Technical Information (OSTI), Juli 2021. http://dx.doi.org/10.2172/1808822.
Der volle Inhalt der QuelleKaffenberger, Michelle, und Marla Spivack. System Coherence for Learning: Applications of the RISE Education Systems Framework. Research on Improving Systems of Education (RISE), Januar 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/086.
Der volle Inhalt der QuelleJiang, Zhong-Ping. Cognitive Models for Learning to Control Dynamic Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada487160.
Der volle Inhalt der QuelleKaffenberger, Michelle, Jason Silberstein und Marla Spivack. Evaluating Systems: Three Approaches for Analyzing Education Systems and Informing Action. Research on Improving Systems of Education (RISE), April 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/093.
Der volle Inhalt der QuelleDeppe, Sahar. AI-based reccomendation system for industrial training. Kompetenzzentrum Arbeitswelt.Plus, Dezember 2023. http://dx.doi.org/10.55594/vmtx7119.
Der volle Inhalt der QuelleRoss-Larson, Bruce. Why Students Aren’t Learning What They Need for a Productive Life. Research on Improving Systems of Education (RISE), März 2023. http://dx.doi.org/10.35489/bsg-rise-2023/pe13.
Der volle Inhalt der QuelleWilinski, Mateusz. Learning of the full dynamic system state matrix from partial PMU observations. Office of Scientific and Technical Information (OSTI), März 2022. http://dx.doi.org/10.2172/1853893.
Der volle Inhalt der QuelleYuan, Yuxuan, Zhaoyu Wang, Ian Dobson, Venkataramana Ajjarapu, Jie Chen und Neeraj Nayak. Robust Learning of Dynamic Interactions for Enhancing Power System Resilience Final Scientific Report. Office of Scientific and Technical Information (OSTI), März 2022. http://dx.doi.org/10.2172/1878168.
Der volle Inhalt der QuelleOsypova, Nataliia V., und Volodimir I. Tatochenko. Improving the learning environment for future mathematics teachers with the use application of the dynamic mathematics system GeoGebra AR. [б. в.], Juli 2021. http://dx.doi.org/10.31812/123456789/4628.
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