Stochastic Systems
Stochastic Systems

Author: P. R. Kumar

Publisher: SIAM

Published: 2015-12-15

Total Pages: 371

ISBN-13: 1611974267

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Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.?

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

Author: L. Gerencser

Publisher: Springer

Published: 1991-07-25

Total Pages: 405

ISBN-13: 9783540541332

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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.

Identification and Stochastic Adaptive Control
Identification and Stochastic Adaptive Control

Author: Han-fu Chen

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 436

ISBN-13: 1461204291

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Identifying the input-output relationship of a system or discovering the evolutionary law of a signal on the basis of observation data, and applying the constructed mathematical model to predicting, controlling or extracting other useful information constitute a problem that has been drawing a lot of attention from engineering and gaining more and more importance in econo metrics, biology, environmental science and other related areas. Over the last 30-odd years, research on this problem has rapidly developed in various areas under different terms, such as time series analysis, signal processing and system identification. Since the randomness almost always exists in real systems and in observation data, and since the random process is sometimes used to model the uncertainty in systems, it is reasonable to consider the object as a stochastic system. In some applications identification can be carried out off line, but in other cases this is impossible, for example, when the structure or the parameter of the system depends on the sample, or when the system is time-varying. In these cases we have to identify the system on line and to adjust the control in accordance with the model which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners.

Linear Stochastic Systems
Linear Stochastic Systems

Author: Peter E. Caines

Publisher: SIAM

Published: 2018-06-12

Total Pages: 892

ISBN-13: 1611974712

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Linear Stochastic Systems, originally published in 1988, is today as comprehensive a reference to the theory of linear discrete-time-parameter systems as ever. Its most outstanding feature is the unified presentation, including both input-output and state space representations of stochastic linear systems, together with their interrelationships. The author first covers the foundations of linear stochastic systems and then continues through to more sophisticated topics including the fundamentals of stochastic processes and the construction of stochastic systems; an integrated exposition of the theories of prediction, realization (modeling), parameter estimation, and control; and a presentation of stochastic adaptive control theory. Written in a clear, concise manner and accessible to graduate students, researchers, and teachers, this classic volume also includes background material to make it self-contained and has complete proofs for all the principal results of the book. Furthermore, this edition includes many corrections of errata collected over the years.

Linear Stochastic Systems
Linear Stochastic Systems

Author: Peter Caines

Publisher:

Published: 1988

Total Pages: 936

ISBN-13:

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This text focuses on linear stochastic models, whose theoretical foundations are the most fully worked out and the most frequently applied area of systems and control theory. Presents a unified and mathematically rigorous exposition of the main results of the theory of linear discrete-time-parameter stochastic systems. Begins with a thorough examination of the fundamentals of stochastic processes and the construction of stochastic systems, and goes on to provide an integrated treatment of the theories of prediction, regulation, modeling and estimation of system dynamics (system identification), and control. Text concludes with a presentation of stochastic adaptive control theory. Coverage of all topics incorporates the most recent research in the field.

Stochastic Theory and Adaptive Control
Stochastic Theory and Adaptive Control

Author: T. E. Duncan

Publisher: Springer

Published: 1992

Total Pages: 526

ISBN-13:

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This workshop on stochastic theory and adaptive control assembled many of the leading researchers on stochastic control and stochastic adaptive control to increase scientific exchange and cooperative research between these two subfields of stochastic analysis. The papers included in the proceedings include survey and research. They describe both theoretical results and applications of adaptive control. There are theoretical results in identification, filtering, control, adaptive control and various other related topics. Some applications to manufacturing systems, queues, networks, medicine and other topics are gien.