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AuthorDuflo, Marie. author
TitleRandom Iterative Models [electronic resource] / by Marie Duflo
ImprintBerlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1997
Connect tohttp://dx.doi.org/10.1007/978-3-662-12880-0
Descript XV, 385 p. online resource

SUMMARY

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


CONTENT

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


Mathematics Algorithms Probabilities Mathematics Probability Theory and Stochastic Processes Algorithms Mathematics general



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