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Cognitive dynamic systems : perception--action cycle, radar, and radio /
"The principles of cognition are becoming increasingly important in the areas of signal processing, communications and control. In this groundbreaking book, Simon Haykin, a pioneer in the field and an award-winning researcher, educator and author, sets out the fundamental ideas of cognitive dyn...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge ; New York :
Cambridge University Press,
2012.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Haykin, Simon S., |d 1931- |1 https://id.oclc.org/worldcat/entity/E39PBJgH9XhBVDFr9TGW8vYtrq | |
| 245 | 1 | 0 | |a Cognitive dynamic systems : |b perception--action cycle, radar, and radio / |c Simon Haykin. |
| 260 | |a Cambridge ; |a New York : |b Cambridge University Press, |c 2012. | ||
| 300 | |a 1 online resource (xii, 309 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references (pages 297-305) and index. | ||
| 505 | 0 | |a 1. Introduction -- 2. The perception-action cycle -- 3. Power-spectrum estimation for sensing the environment -- 4. Bayesian filtering for state estimation of the environment -- 5. Dynamic programming for action in the environment -- 6. Cognitive radar -- 7. Cognitive radio -- 8. Epilogue. | |
| 520 | |a "The principles of cognition are becoming increasingly important in the areas of signal processing, communications and control. In this groundbreaking book, Simon Haykin, a pioneer in the field and an award-winning researcher, educator and author, sets out the fundamental ideas of cognitive dynamic systems. Weaving together the various branches of study involved, he demonstrates the power of cognitive information processing and highlights a range of future research directions. The book begins with a discussion of core topics such as cognition and sensing, dealing, in particular, with the perception-action cycle. Bayesian filtering, machine learning and dynamic programming are then addressed. Building on these foundations, there is detailed coverage of two important practical applications, cognitive radar and cognitive radio. Blending theory and practice, this insightful book is aimed at all graduate students and researchers looking for a thorough grounding in this fascinating field"-- |c Provided by publisher | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Self-organizing systems. | |
| 650 | 0 | |a Cognitive radio networks. | |
| 650 | 6 | |a Systèmes auto-organisés. | |
| 650 | 6 | |a Radio cognitive. | |
| 650 | 7 | |a SCIENCE |x System Theory. |2 bisacsh | |
| 650 | 7 | |a Cognitive radio networks |2 fast | |
| 650 | 7 | |a Self-organizing systems |2 fast | |
| 650 | 7 | |a Kognitiver Funk |2 gnd | |
| 650 | 7 | |a Selbst organisierendes System |2 gnd | |
| 758 | |i has work: |a Cognitive dynamic systems (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFQvpFPhhpcPHF69wKKHfm |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Haykin, Simon S., 1931- |t Cognitive dynamic systems. |d Cambridge ; New York : Cambridge University Press, 2012 |z 9780521114363 |w (DLC) 2011037991 |w (OCoLC)714728195 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=862348 |z Texto completo |
| 880 | 8 | |6 505-00/(S |a Notes and practical references -- 5: Dynamic programming for action in the environment -- 5.1 Markov decision processes -- 5.1.1 The basic problem -- 5.2 Bellman's optimality criterion -- 5.2.1 Dynamic-programming algorithm -- 5.2.2 Bellman's optimality equation -- 5.3 Policy iteration -- 5.3.1 Formulation of the policy iteration algorithm -- 5.4 Value iteration -- 5.4.1 Formulation of the value iteration algorithm -- 5.5 Approximate dynamic programming for problems with imperfect state information -- 5.5.1 Basics of problems with imperfect state information -- 5.5.2 Reformulation of the imperfect state-information problem as a perfect state-information problem -- 5.6 Reinforcement learning viewed as approximate dynamic programming -- 5.7 Q-learning -- 5.7.1 Summarizing remarks -- 5.8 Temporal-difference learning -- 5.8.1 Multistep TD learning -- 5.8.2 Eligible traces -- 5.8.3 Two limiting cases of TD learning -- 5.8.4 Summarizing remarks -- 5.9 On the relationships between temporal-difference learning and dynamic programming -- 5.9.1 λ-return -- 5.10 Linear function approximations of dynamic programming -- 5.11 Linear GQ(λ) for predictive learning -- 5.11.1 Objective function setting the stage for approximation -- 5.11.2 The GQ(λ) algorithm -- 5.11.3 Weight-doubling trick -- 5.11.4 Eligibility traces vector -- 5.11.5 New action-state feature vector -- 5.11.6 Summarizing remarks -- 5.11.7 Practical considerations -- 5.12 Summary and discussion -- 5.12.1 Bellman's dynamic programming -- 5.12.2 Imperfect state information -- 5.12.3 Reinforcement learning -- 5.12.4 Linear GQ(k) algorithm -- 5.12.5 Greedy-GQ -- 5.12.6 New generation of approximate dynamic programming algorithms: linear GQ methods -- Notes and practical references -- 6: Cognitive radar -- 6.1 Three classes of radars defined -- 6.2 The perception-action cycle. | |
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