Adaptive Algorithms and Stochastic Approximations (Stochastic Modelling and Applied Probability, 22, Band 22) - Primera edición
1990, ISBN: 9783540528944
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1990, ISBN: 9783540528944
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1990, ISBN: 3540528946
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1990, ISBN: 3540528946
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1990, ISBN: 9783540528944
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Adaptive Algorithms and Stochastic Approximations (Stochastic Modelling and Applied Probability, 22, Band 22) - Primera edición
1990, ISBN: 9783540528944
Pasta dura
Übersetzer: Wilson, S.S. Springer, Gebundene Ausgabe, Auflage: 1, 376 Seiten, Publiziert: 1990-12-05T00:00:01Z, Produktgruppe: Buch, 1.58 kg, Verkaufsrang: 3530279, Informatik, IT-Ausbild… Más…
Benveniste, Albert; Metivier, Michel; Priouret, Pierre:
Adaptive Algorithms and Stochastic Approximations. - encuadernado, tapa blanda1990, ISBN: 9783540528944
[PU: Berlin, Heidelberg, New York: Springer-Verlag 1990], With 24 Figures, XI, 365 S., gebundene Ausgabe Applications of Mathematics, Band 22. Zust: Gutes Exemplar., DE, [SC: 3.00], gebra… Más…
1990
ISBN: 3540528946
[EAN: 9783540528944], [SC: 9.95], [PU: Berlin, Heidelberg, New York: Springer-Verlag], Applications of Mathematics, Band 22. Zust: Gutes Exemplar. With 24 Figures, XI, 365 S., Englisch 71… Más…
1990, ISBN: 3540528946
[EAN: 9783540528944], [SC: 3.0], [PU: Berlin, Heidelberg, New York: Springer-Verlag], Applications of Mathematics, Band 22. Zust: Gutes Exemplar. With 24 Figures, XI, 365 S., Englisch 712… Más…
1990, ISBN: 9783540528944
With 24 Figures, XI, 365 S.,gebundene Ausgabe Applications of Mathematics, Band 22. Zust: Gutes Exemplar. Versand D: 3,00 EUR, [PU:Berlin, Heidelberg, New York: Springer-Verlag]
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Detalles del libro - Adaptive Algorithms and Stochastic Approximations (Stochastic Modelling and Applied Probability, 22, Band 22)
EAN (ISBN-13): 9783540528944
ISBN (ISBN-10): 3540528946
Tapa dura
Año de publicación: 1990
Editorial: Springer
Libro en la base de datos desde 2007-06-01T13:24:41-05:00 (Mexico City)
Página de detalles modificada por última vez el 2024-04-23T20:19:06-06:00 (Mexico City)
ISBN/EAN: 3540528946
ISBN - escritura alterna:
3-540-52894-6, 978-3-540-52894-4
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: benveniste, michel metivier, michel albert, benvéniste, michel pierre
Título del libro: approximations, stochastic approximation, algorithms
Datos del la editorial
Autor: Albert Benveniste; Michel Metivier; Pierre Priouret
Título: Stochastic Modelling and Applied Probability; Adaptive Algorithms and Stochastic Approximations
Editorial: Springer; Springer Berlin
364 Páginas
Año de publicación: 1990-12-05
Berlin; Heidelberg; DE
Traductor: S.S. Wilson (Francés)
Peso: 0,715 kg
Idioma: Inglés
85,55 € (DE)
87,95 € (AT)
106,60 CHF (CH)
Not available, publisher indicates OP
BB; Book; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; Rang; stochastic approximation; Moment; statistics; System identification; behavior; pattern recognition; Markov chain; Extension; control; cognition; Parameter; ergodicity; modeling; intelligence; A; Probability Theory and Stochastic Processes; Mathematics and Statistics; Math. Applications in Chemistry; Computational Intelligence; Stochastik; Mathematik für Wissenschaftler; Chemie; Künstliche Intelligenz; BC; EA
I. Adaptive Algorithms: Applications.- 1. General Adaptive Algorithm Form.- 1.1 Introduction.- 1.2 Two Basic Examples and Their Variants.- 1.3 General Adaptive Algorithm Form and Main Assumptions.- 1.4 Problems Arising.- 1.5 Summary of the Adaptive Algorithm Form: Assumptions (A).- 1.6 Conclusion.- 1.7 Exercises.- 1.8 Comments on the Literature.- 2. Convergence: the ODE Method.- 2.1 Introduction.- 2.2 Mathematical Tools: Informal Introduction.- 2.3 Guide to the Analysis of Adaptive Algorithms.- 2.4 Guide to Adaptive Algorithm Design.- 2.5 The Transient Regime.- 2.6 Conclusion.- 2.7 Exercises.- 2.8 Comments on the Literature.- 3. Rate of Convergence.- 3.1 Mathematical Tools: Informal Description.- 3.2 Applications to the Design of Adaptive Algorithms with Decreasing Gain.- 3.3 Conclusions from Section 3.2.- 3.4 Exercises.- 3.5 Comments on the Literature.- 4. Tracking Non-Stationary Parameters.- 4.1 Tracking Ability of Algorithms with Constant Gain.- 4.2 Multistep Algorithms.- 4.3 Conclusions.- 4.4 Exercises.- 4.5 Comments on the Literature.- 5. Sequential Detection; Model Validation.- 5.1 Introduction and Description of the Problem.- 5.2 Two Elementary Problems and their Solution.- 5.3 Central Limit Theorem and the Asymptotic Local Viewpoint.- 5.4 Local Methods of Change Detection.- 5.5 Model Validation by Local Methods.- 5.6 Conclusion.- 5.7 Annex: Proofs of Theorems 1 and 2.- 5.8 Exercises.- 5.9 Comments on the Literature.- 6. Appendices to Part I.- 6.1 Rudiments of Systems Theory.- 6.2 Second Order Stationary Processes.- 6.3 Kaiman Filters.- II. Stochastic Approximations: Theory.- 1. O.D.E. and Convergence A.S. for an Algorithm with Locally Bounded Moments.- 1.1 Introduction of the General Algorithm.- 1.2 Assumptions Peculiar to Chapter 1.- 1.3 Decomposition of the General Algorithm.- 1.4 L2 Estimates.- 1.5 Approximation of the Algorithm by the Solution of the O.D.E.- 1.6 Asymptotic Analysis of the Algorithm.- 1.7 An Extension of the Previous Results.- 1.8 Alternative Formulation of the Convergence Theorem.- 1.9 A Global Convergence Theorem.- 1.10 Rate of L2 Convergence of Some Algorithms.- 1.11 Comments on the Literature.- 2. Application to the Examples of Part I.- 2.1 Geometric Ergodicity of Certain Markov Chains.- 2.2 Markov Chains Dependent on a Parameter ?.- 2.3 Linear Dynamical Processes.- 2.4 Examples.- 2.5 Decision-Feedback Algorithms with Quantisation.- 2.6 Comments on the Literature.- 3. Analysis of the Algorithm in the General Case.- 3.1 New Assumptions and Control of the Moments.- 3.2 Lq Estimates.- 3.3 Convergence towards the Mean Trajectory.- 3.4 Asymptotic Analysis of the Algorithm.- 3.5 “Tube of Confidence” for an Infinite Horizon.- 3.6 Final Remark. Connections with the Results of Chapter 1.- 3.7 Comments on the Literature.- 4. Gaussian Approximations to the Algorithms.- 4.1 Process Distributions and their Weak Convergence.- 4.2 Diffusions. Gaussian Diffusions.- 4.3 The Process U?(t) for an Algorithm with Constant Step Size.- 4.4 Gaussian Approximation of the Processes U?(t).- 4.5 Gaussian Approximation for Algorithms with Decreasing Step Size.- 4.6 Gaussian Approximation and Asymptotic Behaviour of Algorithms with Constant Steps.- 4.7 Remark on Weak Convergence Techniques.- 4.8 Comments on the Literature.- 5. Appendix to Part II: A Simple Theorem in the “Robbins-Monro” Case.- 5.1 The Algorithm, the Assumptions and the Theorem.- 5.2 Proof of the Theorem.- 5.3 Variants.- Subject Index to Part I.- Subject Index to Part II.Adaptation is now recognised as a keystone of "intelligence" within computerised systems. This book is a reference work, both for engineers who use stochastic approximations in terms of problems arising from real applications. Relevant application areas are adaptive filtering and more generally adaptive signal processing, systems identification and adaptive control, and several aspects of pattern recognition and machine intelligence.
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