Ebook: Topics in Stochastic Systems: Modelling, Estimation and Adaptive Control

This book contains a collection of survey papers in the areas of modelling, estimation and adaptive control of stochastic systems describing recent efforts to develop a systematic and elegant theory of identification and adaptive control. It is meant to provide a fast introduction to some of the recent achievements. The book is intended for graduate students and researchers interested in statistical problems of control in general. Students in robotics and communication will also find it valuable. Readers are expected to be familiar with the fundamentals of probability theory and stochastic processes.

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This book contains a collection of survey papers in the areas of modelling, estimation and adaptive control of stochastic systems describing recent efforts to develop a systematic and elegant theory of identification and adaptive control. It is meant to provide a fast introduction to some of the recent achievements. The book is intended for graduate students and researchers interested in statistical problems of control in general. Students in robotics and communication will also find it valuable. Readers are expected to be familiar with the fundamentals of probability theory and stochastic processes.

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This book contains a collection of survey papers in the areas of modelling, estimation and adaptive control of stochastic systems describing recent efforts to develop a systematic and elegant theory of identification and adaptive control. It is meant to provide a fast introduction to some of the recent achievements. The book is intended for graduate students and researchers interested in statistical problems of control in general. Students in robotics and communication will also find it valuable. Readers are expected to be familiar with the fundamentals of probability theory and stochastic processes.
Content:
Front Matter....Pages -
Direct modeling of white noise in stochastic systems....Pages 1-30
Markovian representations of cyclostationary processes....Pages 31-46
Parametriztions of linear stochastic systems....Pages 47-65
Stochastic realization for approximate modeling....Pages 66-78
Representation of inner products and stochastic realization....Pages 79-102
On realization and identification of stochastic bilinear systems....Pages 103-115
On stochastic partial differential equations. Results on approximations....Pages 116-136
Developments in parameter bounding....Pages 137-158
Recent progress in parallel stochastic approximations....Pages 159-184
On the adaptive stabilization and ergodic behaviour of stochastic systems with jump-Markov parameters via nonlinear filtering....Pages 185-215
Identification and adaptive control for ARMAX systems....Pages 216-241
Some methods for the adaptive control of continuous time linear stochastic systems....Pages 242-267
Strong approximation results in estimation and adaptive control....Pages 268-299
Stochastic adaptive control: Results and perspective....Pages 300-334
Information bounds, certainty equivalence and learning in asymptotically efficient adaptive control of time-invariant stochastic systems....Pages 335-368
Stability of Markov chains on topological spaces with applications to adaptive control and time series analysis....Pages 369-401

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This book contains a collection of survey papers in the areas of modelling, estimation and adaptive control of stochastic systems describing recent efforts to develop a systematic and elegant theory of identification and adaptive control. It is meant to provide a fast introduction to some of the recent achievements. The book is intended for graduate students and researchers interested in statistical problems of control in general. Students in robotics and communication will also find it valuable. Readers are expected to be familiar with the fundamentals of probability theory and stochastic processes.
Content:
Front Matter....Pages -
Direct modeling of white noise in stochastic systems....Pages 1-30
Markovian representations of cyclostationary processes....Pages 31-46
Parametriztions of linear stochastic systems....Pages 47-65
Stochastic realization for approximate modeling....Pages 66-78
Representation of inner products and stochastic realization....Pages 79-102
On realization and identification of stochastic bilinear systems....Pages 103-115
On stochastic partial differential equations. Results on approximations....Pages 116-136
Developments in parameter bounding....Pages 137-158
Recent progress in parallel stochastic approximations....Pages 159-184
On the adaptive stabilization and ergodic behaviour of stochastic systems with jump-Markov parameters via nonlinear filtering....Pages 185-215
Identification and adaptive control for ARMAX systems....Pages 216-241
Some methods for the adaptive control of continuous time linear stochastic systems....Pages 242-267
Strong approximation results in estimation and adaptive control....Pages 268-299
Stochastic adaptive control: Results and perspective....Pages 300-334
Information bounds, certainty equivalence and learning in asymptotically efficient adaptive control of time-invariant stochastic systems....Pages 335-368
Stability of Markov chains on topological spaces with applications to adaptive control and time series analysis....Pages 369-401
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