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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
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.