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Mathematics - Probability Theory and Stochastic Processes | Random Iterative Models

Random Iterative Models

Duflo, Marie

Translated by Wilson, S.S.

1997, XV, 385 p.

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  • About this book

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 Level » Research

Keywords » (recursive - and functional -) control - Markov chain - Markov model - aaptive tracking - causality - estimation - linear systems - markov chains - stochastic approximation

Related subjects » Computational Science & Engineering - Mathematics - Probability Theory and Stochastic Processes

Table of contents 

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.

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