UID:
almahu_9947363276402882
Umfang:
XV, 385 p.
,
online resource.
ISBN:
9783662128800
Serie:
Stochastic Modelling and Applied Probability, 34
Inhalt:
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.
Anmerkung:
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.
In:
Springer eBooks
Weitere Ausg.:
Printed edition: ISBN 9783642081750
Sprache:
Englisch
Schlagwort(e):
Electronic books.
DOI:
10.1007/978-3-662-12880-0
URL:
http://dx.doi.org/10.1007/978-3-662-12880-0
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