Relevant bibliographies by topics / Learning dynamical systems

Academic literature on the topic 'Learning dynamical systems'

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Published: 25 May 2024

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Journal articles on the topic "Learning dynamical systems"

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Hein, Helle, and Ulo Lepik. "LEARNING TRAJECTORIES OF DYNAMICAL SYSTEMS." Mathematical Modelling and Analysis 17, no. 4 (September 1, 2012): 519–31. http://dx.doi.org/10.3846/13926292.2012.706654.

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The aim of the present paper is to describe the method that is capable of adjusting the parameters of a dynamical system so that the trajectories gain certain specified properties. Three problems are considered: (i) learning fixed points, (ii) learning to periodic trajectories, (iii) restrictions on the trajectories. An error function, which measures the discrepancy between the actual and desired trajectories is introduced. Numerical results of several examples, which illustrate the efficiency of the method, are presented.
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Khadivar, Farshad, Ilaria Lauzana, and Aude Billard. "Learning dynamical systems with bifurcations." Robotics and Autonomous Systems 136 (February 2021): 103700. http://dx.doi.org/10.1016/j.robot.2020.103700.

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Berry, Tyrus, and Suddhasattwa Das. "Learning Theory for Dynamical Systems." SIAM Journal on Applied Dynamical Systems 22, no. 3 (August 8, 2023): 2082–122. http://dx.doi.org/10.1137/22m1516865.

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Roy, Sayan, and Debanjan Rana. "Machine Learning in Nonlinear Dynamical Systems." Resonance 26, no. 7 (July 2021): 953–70. http://dx.doi.org/10.1007/s12045-021-1194-0.

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WANG, CONG, TIANRUI CHEN, GUANRONG CHEN, and DAVID J. HILL. "DETERMINISTIC LEARNING OF NONLINEAR DYNAMICAL SYSTEMS." International Journal of Bifurcation and Chaos 19, no. 04 (April 2009): 1307–28. http://dx.doi.org/10.1142/s0218127409023640.

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In this paper, we investigate the problem of identifying or modeling nonlinear dynamical systems undergoing periodic and period-like (recurrent) motions. For accurate identification of nonlinear dynamical systems, the persistent excitation condition is normally required to be satisfied. Firstly, by using localized radial basis function networks, a relationship between the recurrent trajectories and the persistence of excitation condition is established. Secondly, for a broad class of recurrent trajectories generated from nonlinear dynamical systems, a deterministic learning approach is presented which achieves locally-accurate identification of the underlying system dynamics in a local region along the recurrent trajectory. This study reveals that even for a random-like chaotic trajectory, which is extremely sensitive to initial conditions and is long-term unpredictable, the system dynamics of a nonlinear chaotic system can still be locally-accurate identified along the chaotic trajectory in a deterministic way. Numerical experiments on the Rossler system are included to demonstrate the effectiveness of the proposed approach.
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Ahmadi, Amir Ali, and Bachir El Khadir. "Learning Dynamical Systems with Side Information." SIAM Review 65, no. 1 (February 2023): 183–223. http://dx.doi.org/10.1137/20m1388644.

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Grigoryeva, Lyudmila, Allen Hart, and Juan-Pablo Ortega. "Learning strange attractors with reservoir systems." Nonlinearity 36, no. 9 (July 27, 2023): 4674–708. http://dx.doi.org/10.1088/1361-6544/ace492.

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Abstract This paper shows that the celebrated embedding theorem of Takens is a particular case of a much more general statement according to which, randomly generated linear state-space representations of generic observations of an invertible dynamical system carry in their wake an embedding of the phase space dynamics into the chosen Euclidean state space. This embedding coincides with a natural generalized synchronization that arises in this setup and that yields a topological conjugacy between the state-space dynamics driven by the generic observations of the dynamical system and the dynamical system itself. This result provides additional tools for the representation, learning, and analysis of chaotic attractors and sheds additional light on the reservoir computing phenomenon that appears in the context of recurrent neural networks.
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Davids, Keith. "Learning design for Nonlinear Dynamical Movement Systems." Open Sports Sciences Journal 5, no. 1 (September 13, 2012): 9–16. http://dx.doi.org/10.2174/1875399x01205010009.

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Campi, M. C., and P. R. Kumar. "Learning dynamical systems in a stationary environment." Systems & Control Letters 34, no. 3 (June 1998): 125–32. http://dx.doi.org/10.1016/s0167-6911(98)00005-x.

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Rajendra, P., and V. Brahmajirao. "Modeling of dynamical systems through deep learning." Biophysical Reviews 12, no. 6 (November 22, 2020): 1311–20. http://dx.doi.org/10.1007/s12551-020-00776-4.

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Dissertations / Theses on the topic "Learning dynamical systems"

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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/.

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Learning Classifier Systems (LCS) traditionally use a ternary encoding to generalise over the environmental inputs and to associate appropriate actions. However, a number of schemes have been presented beyond this, ranging from integers to artificial neural networks. This thesis investigates the use of Dynamical Genetic Programming (DGP) as a knowledge representation within LCS. DGP is a temporally dynamic, graph-based, symbolic representation. Temporal dynamism has been identified as an important aspect in biological systems, artificial life, and cognition in general. Furthermore, discrete dynamical systems have been found to exhibit inherent content-addressable memory. In this thesis, the collective emergent behaviour of ensembles of such dynamical function networks are herein shown to be exploitable toward solving various computational tasks. Significantly, it is shown possible to exploit the variable-length, adaptive memory existing inherently within the networks under an asynchronous scheme, and where all new parameters introduced are self-adaptive. It is shown possible to exploit the collective mechanics to solve both discrete and continuous-valued reinforcement learning problems, and to perform symbolic regression. In particular, the representation is shown to provide improved performance beyond a traditional Genetic Programming benchmark on a number of a composite polynomial regression tasks. Superior performance to previously published techniques is also shown in a continuous-input-output reinforcement learning problem. Finally, it is shown possible to perform multi-step-ahead predictions of a financial time-series by repeatedly sampling the network states at succeeding temporal intervals.
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Ferizbegovic, 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.

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We consider the linear quadratic regulation problem when the plant is an unknown linear dynamical system. We present robust model-based methods based on convex optimization, which minimize the worst-case cost with respect to uncertainty around model estimates. To quantify uncertainty, we derive a methodbased on Bayesian inference, which is directly applicable to robust control synthesis.We focus on control policies that can be iteratively updated after sequentially collecting data. More specifically, we seek to design control policies that balance exploration (reducing model uncertainty) and exploitation (control of the system) when exploration must be safe (robust).First, we derive a robust controller to minimize the worst-case cost, with high probability, given the empirical observation of the system. This robust controller synthesis is then used to derive a robust dual controller, which updates its control policy after collecting data. An episode in which data is collected is called exploration, and the episode using an updated control policy is exploitation. The objective is to minimize the worst-case cost of the updated control policy, requiring that a given exploration budget constrains the worst-case cost during exploration.We look into robust dual control in both finite and infinite horizon settings. The main difference between the finite and infinite horizon settings is that the latter does not consider the length of the exploration and exploitation phase, but it rather approximates the cost using the infinite horizon cost. In the finite horizon setting, we discuss how different exploration lengths affect the trade-off between exploration and exploitation.Additionally, we derive methods that balance exploration and exploitation to minimize the cumulative worst-case cost for a fixed number of episodes. In this thesis, we refer to such a problem as robust reinforcement learning. Essentially, it is a robust dual controller aiming to minimize the cumulative worst-case cost, and that updates its control policy in each episode.Numerical experiments show that the proposed methods have better performance compared to existing state-of-the-art algorithms. Moreover, experiments also indicate that the exploration prioritizes the uncertainty reduction in the parameters that matter most for control.

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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.

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System identification has always been one of the main research focuses of the control community, since the early steps of the automatic control field. The development of a dynamical system’s models from experimental data is instrumental for understanding the plant under study and designing its model-based control scheme. In the last decade, a cross-fertilization began between the System Identification and the Statistical Learning communities. This led firstly to the introduction of regularization techniques in system identification, and, more recently, to the application of kernel methods to dynamical system learning. This thesis further investigates the roles that learning methods can have in the control science. In the first part, we lay the theoretical foundations of a new kernel-based regularization method for Nonlinear Finite Impulse Response (NFIR) system identification. The method, called Semi-Supervised Identification (SSI), relies on the manifold spanned by the system’s inputs. This manifold is constructed by using not only the measured input/output data, but also inputs data for which there is no corresponding outputs. The effect of this rationale is to impose prior information on the system structure, in the form of local smoothness assumptions. This differs from standard Tikhonov regularization, which imposes a global smoothness behaviour on the learned function. The second part of this work presents practical applications of how statistical learning methods can be used to face control and estimation problems. The case studies span a variety of different applications, from fault detection of electro-mechanical actuators, to clustering methodologies and pure forecasting challanges.
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Izquierdo, 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.

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Mussmann, 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.

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Lindsten, 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.

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Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods.Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated state-trajectory. A specific particle MCMC algorithm, referred to as particle Gibbs with ancestor sampling (PGAS), is suggested. By making use of backward sampling ideas, albeit implemented in a forward-only fashion, PGAS enjoys good mixing even when using seemingly few particles in the underlying SMC sampler. This results in a computationally competitive particle MCMC algorithm. As illustrated in this thesis, PGAS is a useful tool for both Bayesian and frequentistic parameter inference as well as for state smoothing. The PGAS sampler is successfully applied to the classical problem of Wiener system identification, and it is also used for inference in the challenging class of non-Markovian latent variable models.Many nonlinear models encountered in practice contain some tractable substructure. As a second problem considered in this thesis, we develop Monte Carlo methods capable of exploiting such substructures to obtain more accurate estimators than what is provided otherwise. For the filtering problem, this can be done by using the well known Rao-Blackwellized particle filter (RBPF). The RBPF is analysed in terms of asymptotic variance, resulting in an expression for the performance gain offered by Rao-Blackwellization. Furthermore, a Rao-Blackwellized particle smoother is derived, capable of addressing the smoothing problem in so called mixed linear/nonlinear state-space models. The idea of Rao-Blackwellization is also used to develop an online algorithm for Bayesian parameter inference in nonlinear state-space models with affine parameter dependencies.
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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.

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Passey, Jr David Joseph. "Growing Complex Networks for Better Learning of Chaotic Dynamical Systems." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8146.

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This thesis advances the theory of network specialization by characterizing the effect of network specialization on the eigenvectors of a network. We prove and provide explicit formulas for the eigenvectors of specialized graphs based on the eigenvectors of their parent graphs. The second portion of this thesis applies network specialization to learning problems. Our work focuses on training reservoir computers to mimic the Lorentz equations. We experiment with random graph, preferential attachment and small world topologies and demonstrate that the random removal of directed edges increases predictive capability of a reservoir topology. We then create a new network model by growing networks via targeted application of the specialization model. This is accomplished iteratively by selecting top preforming nodes within the reservoir computer and specializing them. Our generated topology out-preforms all other topologies on average.
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Bézenac, Emmanuel de. "Modeling physical processes with deep learning : a dynamical systems approach." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS203.

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L'apprentissage profond s'impose comme un outil prédominant pour l'IA, avec de nombreuses applications fructueuses pour des taches où les données sont abondantes et l'accès aux connaissances préalables est difficile. Cependant ce n'est pas encore le cas dans le domaine des sciences naturelles, et encore moins pour l'étude des systèmes dynamiques. En effet, ceux-ci font l'objet d'études depuis des siècles, une quantité considérable de connaissances a ainsi été acquise, et des algorithmes et des méthodes ingénieux ont été développés. Cette thèse a donc deux objectifs principaux. Le premier concerne l'étude du rôle que l'apprentissage profond doit jouer dans ce vaste écosystème de connaissances, de théories et d'outils. Nous tenterons de répondre à cette question générale à travers un problème concret: la modélisation de processus physiques complexes à l'aide de l'apprentissage profond. Le deuxième objectif est en quelque sorte son contraire; il concerne l'analyse les algorithmes d'apprentissage profond à travers le prisme des systèmes dynamiques et des processus physiques, dans le but d'acquérir une meilleure compréhension et de développer de nouveaux algorithmes pour ce domaine
Deep 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
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Appeltant, Lennert. "Reservoir computing based on delay-dynamical systems." Doctoral thesis, Universitat de les Illes Balears, 2012. http://hdl.handle.net/10803/84144.

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Today, except for mathematical operations, our brain functions much faster and more efficient than any supercomputer. It is precisely this form of information processing in neural networks that inspires researchers to create systems that mimic the brain’s information processing capabilities. In this thesis we propose a novel approach to implement these alternative computer architectures, based on delayed feedback. We show that one single nonlinear node with delayed feedback can replace a large network of nonlinear nodes. First we numerically investigate the architecture and performance of delayed feedback systems as information processing units. Then we elaborate on electronic and opto-electronic implementations of the concept. Next to evaluating their performance for standard benchmarks, we also study task independent properties of the system, extracting information on how to further improve the initial scheme. Finally, some simple modifications are suggested, yielding improvements in terms of speed or performance.
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Books on the topic "Learning dynamical systems"

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P, Spencer John, Thomas Michael S. C, and McClelland James L, eds. Toward a unified theory of development: Connectionism and dynamic systems theory re-considered. Oxford: Oxford University Press, 2009.

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Russell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.

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Haridimos, Tsoukas, and Mylonopoulos Nikolaos 1970-, eds. Organizations as knowledge systems: Knowledge, learning, and dynamic capabilities. New York: Palgrave Macmillan, 2004.

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service), SpringerLink (Online, ed. Self-Evolvable Systems: Machine Learning in Social Media. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Group model building: Facilitating team learning using system dynamics. Chichester: J. Wiley, 1996.

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Aldo, Romano, and SpringerLink (Online service), eds. Dynamic Learning Networks: Models and Cases in Action. Boston, MA: Springer-Verlag US, 2009.

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Hiroyuki, Itami. Dynamics of Knowledge, Corporate Systems and Innovation. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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Reinforcement learning and dynamic programming using function approximators. Boca Raton: CRC Press, 2010.

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Sandrock, Jörg. System dynamics in der strategischen Planung: Zur Gestaltung von Geschäftsmodellen im E-Learning. Wiesbaden: Dt. Univ.-Verl., 2006.

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IEEE 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.

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Book chapters on the topic "Learning dynamical systems"

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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.

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Tanaka, Akinori, Akio Tomiya, and 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.

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Golden, 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.

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Gros, 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.

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Bhat, Harish S., and 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.

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Gaucel, Sébastien, Maarten Keijzer, Evelyne Lutton, and 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.

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Vidyasagar, 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.

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Stamovlasis, 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.

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Lagos, Guido, and 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.

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Liu, Xuanwu, Zhao Li, Yuanhui Mao, Lixiang Lai, Ben Gao, Yao Deng, and 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.

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Conference papers on the topic "Learning dynamical systems"

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Sommer, Nicolas, Klas Kronander, and 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.

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Hudson, Joshua, Khachik Sargsyan, Marta D'Elia, and 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.

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Liu, 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.

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Arimoto, S., S. Kawamura, F. Miyazaki, and 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.

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Krause, Andre Frank, Volker Durr, Thomas Schack, and 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.

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Cong Wang, D. J. Hill, and 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.

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Liu, Ren-Huiliu, and 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.

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Quintanilla, Rafael, and 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.

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Huang, Tzu-Kuo, and 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.

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Saveriano, Matteo, and 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.

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Reports on the topic "Learning dynamical systems"

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Siddiqi, Sajid M., Byron Boots, and Geoffrey J. Gordon. A Constraint Generation Approach to Learning Stable Linear Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada480921.

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Rupe, Adam. Learning Implicit Models of Complex Dynamical Systems From Partial Observations. Office of Scientific and Technical Information (OSTI), July 2021. http://dx.doi.org/10.2172/1808822.

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Kaffenberger, Michelle, and Marla Spivack. System Coherence for Learning: Applications of the RISE Education Systems Framework. Research on Improving Systems of Education (RISE), January 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/086.

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In recent decades, education systems in most low- and middle-income countries (LMICs) have rapidly expanded access to schooling, but learning has lagged behind. There are many reasons for low learning in LMICs. Proximate determinants (such as insufficient financing or poor school management) receive much attention, but focus on these often ignores underlying system drivers. In this chapter we use a systems approach to describe underlying system dynamics that drive learning outcomes. To do so, we first describe the RISE education systems framework and then apply it to two cases. In the case of Sobral, Brazil, the systems framework illustrates how a coherent package of reforms, improving upon multiple system components, produced positive outcomes. In the case of Indonesia, a reform that increased teacher pay, but did not change underlying system dynamics, had no impact on learning. The chapter shows how a systems approach can help to understand success, diagnose failure, and inform action to bring about improvements to children’s learning.
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Jiang, 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.

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Kaffenberger, Michelle, Jason Silberstein, and 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.

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While conventional interventions and evaluations address the symptoms of the learning crisis, there is growing acknowledgement that widespread and sustained learning improvements will require systems approaches that diagnose and address the root causes of low learning. This paper presents and applies three methods to evaluate education systems and inform how to improve system coherence for learning. First, we use learning trajectories to evaluate the dynamics of children’s learning in 22 low- and middle-income countries. Second, we present a set of principles called the ALIGNS principles and show how they can be used to evaluate and improve alignment of curricula, assessments, and teacher support and instruction. Finally, we present a systems diagnostic framework and apply it to a program in South Africa, showing how the program takes a systems approach to improve learning. These tools help concretize systems thinking and bring insights to bear on the design and evaluation of policies and programs intended to improve learning.
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Deppe, Sahar. AI-based reccomendation system for industrial training. Kompetenzzentrum Arbeitswelt.Plus, December 2023. http://dx.doi.org/10.55594/vmtx7119.

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Recommendation systems have become a main part of e-learning, reshaping the landscape of digital education. In an era marked by the proliferation of online courses, diverse learning materials, and users with varying needs, these systems offer a dynamic solution. This paper explores recommendation techniques and their role in e-learning and web-based training, delving into their mechanisms, challenges, and opportunities. Moreover, future directions of these systems in e-learning, including the integration of artificial intelligent and emerging technologies, and the quest for transparency and privacy are highlighted. Additionally, a case study is discussed which focuses on providing a recommendation system in order to offer optimal courses for the employees of Weidmüller Interface GmbH & Co. KG.
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7

Ross-Larson, Bruce. Why Students Aren’t Learning What They Need for a Productive Life. Research on Improving Systems of Education (RISE), March 2023. http://dx.doi.org/10.35489/bsg-rise-2023/pe13.

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The RISE program is a seven-year research effort that seeks to understand what features make education systems coherent and effective in their context and how the complex dynamics within a system allow policies to be successful. RISE had research teams in seven countries: Ethiopia, India, Indonesia, Nigeria, Pakistan, Tanzania, and Vietnam. It also commissioned research by education specialists in Chile, Egypt, Kenya, Peru, and South Africa. Those researchers tested ideas about how the determinants of learning lie more in the realm of politics and particularly in the interests of elites. They focused on how the political conditions have (or have not) put learning at the center of education systems (mostly not) while understanding the challenges of doing so. Each country team produced a detailed study pursuing answers to two central research questions: Did the country prioritize learning over access, and if so, during what periods? What role did politics play in the key decisions and how? The full studies detail their analytical frameworks, their data, and sources (generally interviews, government internal documents and reports, and other local and international publications), and the power of their assessments, given their caveats and limitations. Country summaries extract from the full studies how leadership, governance, teaching, and societal engagement are pertinent to student outcomes (see the next page). This synthesis, in line with Levy 2022, draws on the country summaries to detail the salience of goals of national leaders, alliances of stakeholders, missions of education bureaucracies, and expectations of society.
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Wilinski, Mateusz. Learning of the full dynamic system state matrix from partial PMU observations. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1853893.

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Yuan, Yuxuan, Zhaoyu Wang, Ian Dobson, Venkataramana Ajjarapu, Jie Chen, and Neeraj Nayak. Robust Learning of Dynamic Interactions for Enhancing Power System Resilience Final Scientific Report. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1878168.

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Osypova, Nataliia V., and Volodimir I. Tatochenko. Improving the learning environment for future mathematics teachers with the use application of the dynamic mathematics system GeoGebra AR. [б. в.], July 2021. http://dx.doi.org/10.31812/123456789/4628.

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Immersive technologies and, in particular, augmented reality (AR) are rapidly changing the sphere of education, especially in the field of science, technology, engineering, arts and mathematics. High- quality professional training of a future mathematics teacher who is able to meet the challenges that permeate all sides, the realities of the globalizing information society, presupposes reliance on a highly effective learning environment. The purpose of the research is to transform the traditional educational environment for training future mathematics teachers with the use of the GeoGebra AR dynamic mathematics system, the introduction of cloud technologies into the educational process. The educational potential of GeoGebra AR in the system of professional training of future mathematics teachers is analyzed in the paper. Effective and practical tools for teaching mathematics based on GeoGebra AR using interactive models and videos for mixed and distance learning of students are provided. The advantages of the GeoGebra AR dynamic mathematics system are highlighted. The use of new technologies for the creation of didactic innovative resources that improve the process of teaching and learning mathematics is presented on the example of an educational and methodological task, the purpose of which is to create didactic material on the topic “Sections of polyhedra”. While solving it, future teachers of mathematics should develop the following constituent elements: video materials; test tasks for self-control; dynamic models of sections of polyhedra; video instructions for constructing sections of polyhedra and for solving basic problems in the GeoGebra AR system. The article highlights the main characteristics of the proposed educational environment for training future mathematics teachers using the GeoGebra AR dynamic mathematics system: interdisciplinarity, polyprofessionalism, dynamism, multicomponent.
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