Estimation of stochastic input-output models
Estimation of stochastic input-output models

Author: S.D. Gerking

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 98

ISBN-13: 1461343623

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This monograph is a revision of my Indiana University doctoral disserta tion which was completed in April, 1975. Thanks are, therefore, due to the members of my doctoral committee: Saul Pleeter (Chairman), David J. Behling, R. Jeffery Green, Richard L. Pfister, and Elmus Wicker for their helpful comments on previous versions of the manuscript. In addition, I am indebted to the Division of Research and to the Office of Research and Advanced Studies at Indiana University for financial support. As the reader will observe, the techniques developed in Chapters 3 and 4 of this monograph are illustrated using input-output data from West Virginia. These data were generously made available by William H. Miernyk, Director of the Regional Research Institute at West Virginia University. I also wish to acknowledge the Bureau of Business and Eco nomic Research at Arizona State University for providing two research assistants, Kevin A. Nosbisch and Tom R. Rex, who aided in processing the West Virginia data. A third research assistant, Phillip M. Cano, also worked on this project as part of an independent study program taken under my direction during the spring semester of 1975. Finally, I must thank Mary Holguin and Margaret Shumway who expertly typed the final copy of the manuscript. Despite the efforts of all the individuals mentioned above, I assume responsibility for any errors which may remain.

Stochastic Modelling and Control
Stochastic Modelling and Control

Author: M. H. A. Davis

Publisher: Springer

Published: 1985

Total Pages: 416

ISBN-13:

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This book aims to provide a unified treatment of input/output modelling and of control for discrete-time dynamical systems subject to random disturbances. The results presented are of wide applica bility in control engineering, operations research, econometric modelling and many other areas. There are two distinct approaches to mathematical modelling of physical systems: a direct analysis of the physical mechanisms that comprise the process, or a 'black box' approach based on analysis of input/output data. The second approach is adopted here, although of course the properties ofthe models we study, which within the limits of linearity are very general, are also relevant to the behaviour of systems represented by such models, however they are arrived at. The type of system we are interested in is a discrete-time or sampled-data system where the relation between input and output is (at least approximately) linear and where additive random dis turbances are also present, so that the behaviour of the system must be investigated by statistical methods. After a preliminary chapter summarizing elements of probability and linear system theory, we introduce in Chapter 2 some general linear stochastic models, both in input/output and state-space form. Chapter 3 concerns filtering theory: estimation of the state of a dynamical system from noisy observations. As well as being an important topic in its own right, filtering theory provides the link, via the so-called innovations representation, between input/output models (as identified by data analysis) and state-space models, as required for much contemporary control theory.

Stochastic Modelling and Control
Stochastic Modelling and Control

Author: Mark Davis

Publisher: Springer Science & Business Media

Published: 2013-03-08

Total Pages: 405

ISBN-13: 940094828X

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This book aims to provide a unified treatment of input/output modelling and of control for discrete-time dynamical systems subject to random disturbances. The results presented are of wide applica bility in control engineering, operations research, econometric modelling and many other areas. There are two distinct approaches to mathematical modelling of physical systems: a direct analysis of the physical mechanisms that comprise the process, or a 'black box' approach based on analysis of input/output data. The second approach is adopted here, although of course the properties ofthe models we study, which within the limits of linearity are very general, are also relevant to the behaviour of systems represented by such models, however they are arrived at. The type of system we are interested in is a discrete-time or sampled-data system where the relation between input and output is (at least approximately) linear and where additive random dis turbances are also present, so that the behaviour of the system must be investigated by statistical methods. After a preliminary chapter summarizing elements of probability and linear system theory, we introduce in Chapter 2 some general linear stochastic models, both in input/output and state-space form. Chapter 3 concerns filtering theory: estimation of the state of a dynamical system from noisy observations. As well as being an important topic in its own right, filtering theory provides the link, via the so-called innovations representation, between input/output models (as identified by data analysis) and state-space models, as required for much contemporary control theory.

Stochastic Systems
Stochastic Systems

Author: P. R. Kumar

Publisher: SIAM

Published: 2015-12-15

Total Pages: 371

ISBN-13: 1611974259

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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.

Stochastic Models, Estimation, and Control
Stochastic Models, Estimation, and Control

Author: Peter S. Maybeck

Publisher: Academic Press

Published: 1982-08-25

Total Pages: 311

ISBN-13: 0080960030

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This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.