An up-to-date, self-contained review of a wide range of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, organizing multi-access broadcast channels, self-learning of neural networks ...). Suitable for mathematicians (researchers and also students) and engineers.
"synopsis" may belong to another edition of this title.
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.
Text: English (translation)
Original Language: French
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Seller: Der Buchfreund, Wien, Austria
Original-Pappband. Condition: min. Lagersp., g.e. gr8 Original-Pappband en Mathematik, Naturwissenschaften (Applications of Mathematics. Stochastic Modelling and Applied Probability. 34); XV pp., 385 pp. Seller Inventory # 1137
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Seller: Antiquariat Bernhardt, Kassel, Germany
gebundene Ausgabe. Stochastic Modelling and Applied Probability, Band 34. Zust: Gutes Exemplar. Mängelexemplar-Stempel auf Fußschnitt. XV, 385 S., Englisch 714g. Seller Inventory # 489759
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