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Books on the topic 'Learning dynamical systems'

Author: Grafiati

Published: 25 May 2024

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1

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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2

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

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3

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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4

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

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5

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

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6

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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7

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

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8

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

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9

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

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10

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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11

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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12

Smith, Gareth. Dynamic generation of HTML for a web based learning management system. Oxford: Oxford Brookes University, 2004.

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13

Tronci, Eleonora Maria. Novel Damage Assessment Framework for Dynamic Systems through Transfer Learning from Audio Domains. [New York, N.Y.?]: [publisher not identified], 2022.

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14

Buchheit, Stephan. Entstehung und Akkumulation von Dynamic Capabilities: Eine kausalanalytische Betrachtung im System Personalentwicklung. München: Rainer Hampp Verlag, 2013.

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15

Hayes-Roth, Barbara. An architecture for adaptive intelligent systems. Stanford, Calif: Stanford University, Dept. of Computer Science, 1993.

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16

Ramsay, C. N. (Colin N.), Blades Steve, and ebrary Inc, eds. Learning Ext JS: Build dynamic, desktop-style user interfaces for your data-driven Web applications. Birmingham, U.K: Packt Pub., 2008.

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17

Pea, Roy D. Mechanisms for facilitating a vital and dynamic education system: Fundamental roles for education science and technology. [Washington, D.C.?: Technology Assessment Office, 1987.

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18

Pea, Roy D. Mechanisms for facilitating a vital and dynamic education system: Fundamental roles for education science and technology. [Washington, D.C.?: Technology Assessment Office, 1987.

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19

Pea, Roy D. Mechanisms for facilitating a vital and dynamic education system: Fundamental roles for education science and technology. Washington, DC: Office of Technology Assessment, 1988.

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20

Wei, Bin, and Dan Zhang. Learning Control: Applications in Robotics and Complex Dynamical Systems. Elsevier, 2021.

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21

Learning Control: Applications in Robotics and Complex Dynamical Systems. Elsevier, 2020.

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22

Haykin, Simon, Irwin W. Sandberg, James T. Lo, Craig L. Fancourt, José C. Principe, and Shigeru Katagiri. Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives (Adaptive and Learning Systems for Signal Processing, Communications and Control Series). Wiley-Interscience, 2001.

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23

Kutz, J. Nathan, and Steven L. Brunton. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2019.

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24

Kutz, J. Nathan, and Steven L. Brunton. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2022.

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25

Kutz, J. Nathan, and Steven L. Brunton. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2019.

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26

Billard, Aude, Sina Mirrazavi, and Nadia Figueroa. Learning for Adaptive and Reactive Robot Control: A Dynamical Systems Approach. MIT Press, 2022.

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27

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2022.

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28

Ultimate Machine Learning Data Science: Statistical Methods for Building Trading Strategies to Machine Learning, Dynamical Systems, and Control for Beginners. Independently Published, 2022.

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29

Russell, David W. The BOXES Methodology: Black Box Dynamic Control. Springer, 2014.

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30

The BOXES Methodology: Black Box Dynamic Control. Springer, 2012.

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31

Russell, David W. BOXES Methodology Second Edition: Black Box Control of Ill-Defined Systems. Springer International Publishing AG, 2022.

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32

Di Paolo, Ezequiel A., Thomas Buhrmann, and Xabier E. Barandiaran. Mastery: learning to act and perceive. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198786849.003.0004.

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If action and perception depend on the mastery of the laws of sensorimotor contingencies, then any theory of cognition that starts from this premise will not be complete unless it offers an explanation of how such mastery is achieved and of what exactly constitutes it. This chapter takes inspiration from Piaget’s theory of equilibration to develop an account of mastery as the progressive growth and refinement of an agent’s sensorimotor repertoire, involving processes of assimilation and accommodation. A new interpretation is provided of these Piagetian concepts in dynamical systems terms. The resulting theory holds that mastery of sensorimotor skills is both world-involving and nonrepresentational. Mastery does not consist in the accumulation of knowledge about the sensorimotor regularities that the agent is able to enact; rather, it is the ongoing process of equilibration by which the agent continuously adapts to new challenges presented to her by the world.

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33

Brummel, Mark. Learning Dynamics NAV Patterns. Packt Publishing, 2015.

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34

Learning Maya 5: Dynamics. Sybex, 2003.

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35

Schuldberg, David, Ruth Richards, and Shan Guisinger, eds. Chaos and Nonlinear Psychology. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780190465025.001.0001.

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This book, for psychologists, clinicians, social scientists, and the general reader, reveals how chaos and nonlinear dynamics can bring new understanding to everyday topics in social sciences. Contributors are leaders in the intersection of psychology and chaos and complexity theories. Written first for the curious and the nonspecialist, while adding areas for those with a more extensive background, this book offers openness and creative wonder. It is conceptual and user-friendly, built around six themes—which are the main learnings for readers. These are (1) seeing nonlinearity, (2) appreciating emergence, (3) finding patterns, (4) using simple models, (5) intervening nonlinearly, and (6) considering new worldviews. It takes no specialized study—although there is more sophisticated material and optional math for those wishing it; the techie will, in addition, find concepts and diagrams to ponder. The volume intends to engage, at times may startle—whether about the weather, internet, organizations, family dynamics, health, evolution, or falling in love. It reveals how many social, personal, clinical, research, and life phenomena become understandable and can be modeled in the light of nonlinear dynamical systems theory. It even offers a broadening worldview, happening already in other sciences, toward a more dynamic, interconnected, and evolving picture, including process-oriented appreciation of one’s own experience. Readers meet the themes in different guises while learning to read subtle signs and patterns, and intervene like an aikido master in the flow of our dynamic world. The themes, are woven throughout diverse applications and extended in the integrative conclusions.

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36

Learning Maya 6 | Dynamics. Sybex, 2004.

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37

Muneesawang, Paisarn, Ling Guan, Matthew Kyan, and Kambiz Jarrah. Unsupervised Learning: A Dynamic Approach. Wiley & Sons, Incorporated, John, 2014.

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38

Muneesawang, Paisarn, Ling Guan, Matthew Kyan, and Kambiz Jarrah. Unsupervised Learning: A Dynamic Approach. Wiley & Sons, Incorporated, John, 2014.

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39

Gaeta, Giuseppe, and Miguel A. Rodríguez. Lectures on Hyperhamiltonian Dynamics and Physical Applications. Springer, 2017.

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40

Gaeta, Giuseppe, and Miguel A. Rodríguez. Lectures on Hyperhamiltonian Dynamics and Physical Applications. Springer, 2018.

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41

(Editor), Hardimos Tsoukas, and Nikolaos Mylonopoulos (Editor), eds. Organizations as Knowledge Systems: Knowledge, Learning and Dynamic Capabilities. Palgrave Macmillan, 2004.

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42

Tsoukas, H., and N. Mylonopoulos. Organizations As Knowledge Systems: Knowledge, Learning and Dynamic Capabilities. Palgrave Macmillan Limited, 2016.

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43

Advanced Physics Project for Independent Learning (Advanced Physics Project for Independent Learning (APPIL)). 2nd ed. John Murray, 1989.

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44

Advanced Physics Project for Independent Learning (Advanced Physics Project for Independent Learning (APPIL)). 2nd ed. John Murray, 1989.

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45

Advanced Physics Project for Independent Learning (Advanced Physics Project for Independent Learning (APPIL)). 2nd ed. John Murray, 1989.

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46

Advanced Physics Project for Independent Learning (Advanced Physics Project for Independent Learning (APPIL)). 2nd ed. John Murray, 1989.

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47

Advanced Physics Project for Independent Learning (Advanced Physics Project for Independent Learning (APPIL)). John Murray, 1989.

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48

Advanced Physics Project for Independent Learning (Advanced Physics Project for Independent Learning (APPIL)). John Murray, 1989.

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49

Advanced Physics Project for Independent Learning (Advanced Physics Project for Independent Learning (APPIL)). John Murray, 1989.

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50

Advanced Physics Project for Independent Learning (Advanced Physics Project for Independent Learning (APPIL)). 2nd ed. John Murray, 1989.

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