Stochastic Models Estimation And Control

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Stochastic Models Estimation And Control V 1

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Author : Maybeck
language : en
Publisher: Academic Press
Release Date : 1979-07-17

Stochastic Models Estimation And Control V 1 written by Maybeck and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1979-07-17 with Mathematics categories.

Stochastic Models: Estimation and Control: v. 1

Stochastic Models Estimation And Control

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Author : Peter S. Maybeck
language : en
Publisher: Academic Press
Release Date : 1982-08-25

Stochastic Models Estimation And Control written by Peter S. Maybeck and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982-08-25 with Mathematics categories.

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.

Stochastic Models Estimation And Control

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Author : Peter S. Maybeck
language : en
Publisher:
Release Date : 1982

Stochastic Models Estimation And Control written by Peter S. Maybeck and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982 with categories.

Stochastic Models Estimation And Control V 2

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Author : Maybeck
language : en
Publisher: Academic Press
Release Date : 1982-08-10

Stochastic Models Estimation And Control V 2 written by Maybeck and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982-08-10 with Mathematics categories.

Stochastic Models: Estimation and Control: v. 2

Hidden Markov Models

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Author : Robert J Elliott
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-27

Hidden Markov Models written by Robert J Elliott and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-27 with Science categories.

As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors’ general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control.

Linear Stochastic Systems

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Author : Anders Lindquist
language : en
Publisher: Springer
Release Date : 2015-04-24

Linear Stochastic Systems written by Anders Lindquist and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-24 with Science categories.

This book presents a treatise on the theory and modeling of second-order stationary processes, including an exposition on selected application areas that are important in the engineering and applied sciences. The foundational issues regarding stationary processes dealt with in the beginning of the book have a long history, starting in the 1940s with the work of Kolmogorov, Wiener, Cramér and his students, in particular Wold, and have since been refined and complemented by many others. Problems concerning the filtering and modeling of stationary random signals and systems have also been addressed and studied, fostered by the advent of modern digital computers, since the fundamental work of R.E. Kalman in the early 1960s. The book offers a unified and logically consistent view of the subject based on simple ideas from Hilbert space geometry and coordinate-free thinking. In this framework, the concepts of stochastic state space and state space modeling, based on the notion of the conditional independence of past and future flows of the relevant signals, are revealed to be fundamentally unifying ideas. The book, based on over 30 years of original research, represents a valuable contribution that will inform the fields of stochastic modeling, estimation, system identification, and time series analysis for decades to come. It also provides the mathematical tools needed to grasp and analyze the structures of algorithms in stochastic systems theory.

An Introduction To Stochastic Modeling

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Author : Howard M. Taylor
language : en
Publisher: Academic Press
Release Date : 2014-05-10

An Introduction To Stochastic Modeling written by Howard M. Taylor and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-10 with Mathematics categories.

An Introduction to Stochastic Modeling, Revised Edition provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

Stochastic Systems

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Author : P. R. Kumar
language : en
Publisher: SIAM
Release Date : 2015-12-15

Stochastic Systems written by P. R. Kumar and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-15 with Mathematics categories.

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

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Author : Peter S. Maybeck
language : en
Publisher:
Release Date : 2002

Stochastic Models Estimation And Control written by Peter S. Maybeck and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Control theory categories.

Identification And Stochastic Adaptive Control

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Author : Han-fu Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 1991-11

Identification And Stochastic Adaptive Control written by Han-fu Chen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991-11 with Juvenile Nonfiction categories.

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.