Random Iterative Models [electronic resource] /

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.

Bibliographic Details

Main Authors:	Duflo, Marie. author., SpringerLink (Online service)
Format:	Texto biblioteca
Language:	eng
Published:	Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1997
Subjects:	Mathematics., Algorithms., Probabilities., Probability Theory and Stochastic Processes., Mathematics, general.,
Online Access:	http://dx.doi.org/10.1007/978-3-662-12880-0
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