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
