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Random Iterative Models /
The recent development of computation and automation has lead to quick advances in the theory and practice of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, orgainizing multiaccess broadcast channels, self-learning of neural...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
1997.
|
| Colección: | Stochastic modelling and applied probability ;
34. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Duflo, Marie. | |
| 245 | 1 | 0 | |a Random Iterative Models / |c by Marie Duflo. |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg, |c 1997. | ||
| 300 | |a 1 online resource (xv, 387 pages) | ||
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| 490 | 1 | |a Stochastic Modelling and Applied Probability, |x 0172-4568 ; |v 34 | |
| 520 | |a The recent development of computation and automation has lead to quick advances in the theory and practice of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, orgainizing multiaccess broadcast channels, self-learning of neural networks ...). This book provides a wide-angle view of those methods: stochastic approximation, linear and non-linear models, controlled Markov chains, estimation and adaptive control, learning ... Mathematicians familiar with the basics of Probability and Statistics will find here a self-contained account of many approaches to those theories, some of them classical, some of them leading up to current and future research. Each chapter can form the core material for a course of lectures. Engineers having to control complex systems can discover new algorithms with good performances and reasonably easy computation. | ||
| 505 | 0 | |a I. Sources of Recursive Methods -- 1. Traditional Problems -- 2. Rate of Convergence -- 3. Current Problems -- II. Linear Models -- 4. Causality and Excitation -- 5. Linear Identification and Tracking -- III. Nonlinear Models -- 6. Stability -- 7. Nonlinear Identification and Control -- IV. Markov Models -- 8. Recurrence -- 9. Learning. | |
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Mathematics. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 0 | |a Distribution (Probability theory) | |
| 650 | 0 | |a Computer algorithms. | |
| 650 | 6 | |a Mathématiques. | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 6 | |a Distribution (Théorie des probabilités) | |
| 650 | 7 | |a mathematics. |2 aat | |
| 650 | 7 | |a applied mathematics. |2 aat | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a distribution (statistics-related concept) |2 aat | |
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| 650 | 7 | |a Algorithms |2 fast | |
| 650 | 7 | |a Distribution (Probability theory) |2 fast | |
| 650 | 7 | |a Mathematics |2 fast | |
| 758 | |i has work: |a Random iterative models (Text) |1 https://id.oclc.org/worldcat/entity/E39PCH7KXxmDKCP4cBFG464VP3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |z 9783642081750 |
| 830 | 0 | |a Stochastic modelling and applied probability ; |v 34. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=3100395 |z Texto completo |
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