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Multidimensional Stochastic Approximation And Its Applications To Detection And Estimation
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Book Synopsis Multidimensional Stochastic Approximation and Its Applications to Detection and Estimation by : Anthony Katopis
Download or read book Multidimensional Stochastic Approximation and Its Applications to Detection and Estimation written by Anthony Katopis and published by . This book was released on 1973 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Approximation and Its Applications by : Han-Fu Chen
Download or read book Stochastic Approximation and Its Applications written by Han-Fu Chen and published by Springer Science & Business Media. This book was released on 2005-12-30 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimating unknown parameters based on observation data conta- ing information about the parameters is ubiquitous in diverse areas of both theory and application. For example, in system identification the unknown system coefficients are estimated on the basis of input-output data of the control system; in adaptive control systems the adaptive control gain should be defined based on observation data in such a way that the gain asymptotically tends to the optimal one; in blind ch- nel identification the channel coefficients are estimated using the output data obtained at the receiver; in signal processing the optimal weighting matrix is estimated on the basis of observations; in pattern classifi- tion the parameters specifying the partition hyperplane are searched by learning, and more examples may be added to this list. All these parameter estimation problems can be transformed to a root-seeking problem for an unknown function. To see this, let - note the observation at time i. e. , the information available about the unknown parameters at time It can be assumed that the parameter under estimation denoted by is a root of some unknown function This is not a restriction, because, for example, may serve as such a function.
Book Synopsis Stochastic Approximation and Its Applications by : Han-Fu Chen
Download or read book Stochastic Approximation and Its Applications written by Han-Fu Chen and published by Springer. This book was released on 2010-12-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimating unknown parameters based on observation data conta- ing information about the parameters is ubiquitous in diverse areas of both theory and application. For example, in system identification the unknown system coefficients are estimated on the basis of input-output data of the control system; in adaptive control systems the adaptive control gain should be defined based on observation data in such a way that the gain asymptotically tends to the optimal one; in blind ch- nel identification the channel coefficients are estimated using the output data obtained at the receiver; in signal processing the optimal weighting matrix is estimated on the basis of observations; in pattern classifi- tion the parameters specifying the partition hyperplane are searched by learning, and more examples may be added to this list. All these parameter estimation problems can be transformed to a root-seeking problem for an unknown function. To see this, let - note the observation at time i. e. , the information available about the unknown parameters at time It can be assumed that the parameter under estimation denoted by is a root of some unknown function This is not a restriction, because, for example, may serve as such a function.
Book Synopsis Stochastic Approximation and Recursive Estimation by : M. B. Nevel'son
Download or read book Stochastic Approximation and Recursive Estimation written by M. B. Nevel'son and published by American Mathematical Soc.. This book was released on 1976-10-01 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to sequential methods of solving a class of problems to which belongs, for example, the problem of finding a maximum point of a function if each measured value of this function contains a random error. Some basic procedures of stochastic approximation are investigated from a single point of view, namely the theory of Markov processes and martingales. Examples are considered of applications of the theorems to some problems of estimation theory, educational theory and control theory, and also to some problems of information transmission in the presence of inverse feedback.
Book Synopsis Stochastic Approximation and Recursive Algorithms and Applications by : Harold Kushner
Download or read book Stochastic Approximation and Recursive Algorithms and Applications written by Harold Kushner and published by Springer Science & Business Media. This book was released on 2006-05-04 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. This second edition is a thorough revision, although the main features and structure remain unchanged. It contains many additional applications and results as well as more detailed discussion.
Book Synopsis Stochastic Approximation and Optimization of Random Systems by : L. Ljung
Download or read book Stochastic Approximation and Optimization of Random Systems written by L. Ljung and published by Birkhäuser. This book was released on 2012-12-06 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: The DMV seminar "Stochastische Approximation und Optimierung zufalliger Systeme" was held at Blaubeuren, 28. 5. -4. 6. 1989. The goal was to give an approach to theory and application of stochas tic approximation in view of optimization problems, especially in engineering systems. These notes are based on the seminar lectures. They consist of three parts: I. Foundations of stochastic approximation (H. Walk); n. Applicational aspects of stochastic approximation (G. PHug); In. Applications to adaptation :ugorithms (L. Ljung). The prerequisites for reading this book are basic knowledge in probability, mathematical statistics, optimization. We would like to thank Prof. M. Barner and Prof. G. Fischer for the or ganization of the seminar. We also thank the participants for their cooperation and our assistants and secretaries for typing the manuscript. November 1991 L. Ljung, G. PHug, H. Walk Table of contents I Foundations of stochastic approximation (H. Walk) §1 Almost sure convergence of stochastic approximation procedures 2 §2 Recursive methods for linear problems 17 §3 Stochastic optimization under stochastic constraints 22 §4 A learning model; recursive density estimation 27 §5 Invariance principles in stochastic approximation 30 §6 On the theory of large deviations 43 References for Part I 45 11 Applicational aspects of stochastic approximation (G. PHug) §7 Markovian stochastic optimization and stochastic approximation procedures 53 §8 Asymptotic distributions 71 §9 Stopping times 79 §1O Applications of stochastic approximation methods 80 References for Part II 90 III Applications to adaptation algorithms (L.
Book Synopsis Stochastic Approximation by : Cyrus Derman
Download or read book Stochastic Approximation written by Cyrus Derman and published by . This book was released on 1956 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Approximation with correlated data by : David Charles Farden
Download or read book Stochastic Approximation with correlated data written by David Charles Farden and published by . This book was released on 1975 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is devoted to a unified analytical treatment of algorithms that have been proposed for discrete time adaptive signal processing. These algorithms are treated within the framework of the multidimensional Robbins-Montro stochastic approximation procedure. The special form of the Robbins-Monro procedure which is treated herein and the convergence results obtained are of interest in their own right, having applications outside the realm of adaptive signal processing.
Book Synopsis Stochastic Approximation and Recursive Estimation by : Rafail Zalmanovich Hasʹminskii
Download or read book Stochastic Approximation and Recursive Estimation written by Rafail Zalmanovich Hasʹminskii and published by American Mathematical Soc.. This book was released on with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to sequential methods of solving a class of problems to which belongs, for example, the problem of finding a maximum point of a function if each measured value of this function contains a random error. Some basic procedures of stochastic approximation are investigated from a single point of view, namely the theory of Markov processes and martingales. Examples are considered of applications of the theorems to some problems of estimation theory, educational theory and control theory, and also to some problems of information transmission in the presence of inverse feedback.
Book Synopsis Stochastic Approximation by : M. T. Wasan
Download or read book Stochastic Approximation written by M. T. Wasan and published by Cambridge University Press. This book was released on 2004-06-03 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: A rigorous mathematical treatment of the technique for studying the properties of an experimental situation.
Book Synopsis Introduction to Stochastic Search and Optimization by : James C. Spall
Download or read book Introduction to Stochastic Search and Optimization written by James C. Spall and published by John Wiley & Sons. This book was released on 2005-03-11 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: * Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will appeal to both students and researchers. * Features exercises and web links to software and data sets.
Book Synopsis Stochastic Approximation Methods for Constrained and Unconstrained Systems by : H.J. Kushner
Download or read book Stochastic Approximation Methods for Constrained and Unconstrained Systems written by H.J. Kushner and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. Such recu- sive algorithms occur frequently in stochastic and adaptive control and optimization theory and in statistical esti- tion theory. Typically, a sequence {X } of estimates of a n parameter is obtained by means of some recursive statistical th st procedure. The n estimate is some function of the n_l estimate and of some new observational data, and the aim is to study the convergence, rate of convergence, and the pa- metric dependence and other qualitative properties of the - gorithms. In this sense, the theory is a statistical version of recursive numerical analysis. The approach taken involves the use of relatively simple compactness methods. Most standard results for Kiefer-Wolfowitz and Robbins-Monro like methods are extended considerably. Constrained and unconstrained problems are treated, as is the rate of convergence problem. While the basic method is rather simple, it can be elaborated to allow a broad and deep coverage of stochastic approximation like problems. The approach, relating algorithm behavior to qualitative properties of deterministic or stochastic differ ential equations, has advantages in algorithm conceptualiza tion and design. It is often possible to obtain an intuitive understanding of algorithm behavior or qualitative dependence upon parameters, etc., without getting involved in a great deal of deta~l.
Book Synopsis Stochastic Approximation and Optimization of Random Systems by : L. Ljung
Download or read book Stochastic Approximation and Optimization of Random Systems written by L. Ljung and published by . This book was released on 1992-03-31 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic approximation and its applications by : Madanlal T. Wasan
Download or read book Stochastic approximation and its applications written by Madanlal T. Wasan and published by . This book was released on 1967 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 1999 with total page 862 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic approximation and recursive estimation by : Mikhail Borisovich Nevelʹson
Download or read book Stochastic approximation and recursive estimation written by Mikhail Borisovich Nevelʹson and published by . This book was released on 1976 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Properties of Distributed and Communicating Stochastic Approximation Algorithms by : Harold Joseph Kushner
Download or read book Asymptotic Properties of Distributed and Communicating Stochastic Approximation Algorithms written by Harold Joseph Kushner and published by . This book was released on 1986 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: The asymptotic properties of extensions of the type of distributed or decentralized stochastic approximation proposed are developed. Such algorithms have numerous potential applications in decentralized estimation, detection and adaptive control, or in decentralized Monte Carlo simulation for system optimization (where they can exploit th possibilities of parallel processing). The structure involves several isolated processors (recursive algorithms) who communicate to each other asyhnchronously and at random intervals. The asymptotic (small gain) properties are derived. The communication intervals need not be strictly bounded and they and the system noise can depend on the (communicating) system state. State space constraints are also handled. In many applications, the dynamical terms are merely indicator functions, or have other types of discontinuities. The typical such case is also treated, as is the case where there is noise in the communication. The linear stochastic differential equation satisfied by the (interpolated) asymptotic normalized error sequence is derived, and issued to compare alternative algorithms and communication strategies. Weak convergence methods provide the basic tools.