Adaptive Stochastic Methods
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Adaptive Stochastic Methods
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Author : Dmitriĭ Germanovich Arsenʹev
language : en
Publisher: de Gruyter
Release Date : 2018
Adaptive Stochastic Methods written by Dmitriĭ Germanovich Arsenʹev and has been published by de Gruyter this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Mathematics categories.
"This monograph is devoted to developing adaptive stochastic methods of computational mathematics with the use of adaptively controlled computational procedures. We consider the base ideas of the algorithms, ways to synthesise them, and analyse their properties and efficiency while evaluating multidimensional integrals and solving integral equations of the theory of elasticity and the theory of heat conduction. The key feature of the approaches and results presented in this book consists of a comprehensive analysis of mechanisms of utilisation of adaptive control in statistical evaluation procedures, which makes them converge much faster. This book is intended for all students of numerical methods, mathematical statistics, and methods of statistical simulation, as well as for specialists in the fields of computational mathematics and mechanics"--Page v.
Adaptive Stochastic Methods
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Author : Dmitry G. Arseniev
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2018-01-09
Adaptive Stochastic Methods written by Dmitry G. Arseniev and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-09 with Mathematics categories.
This monograph develops adaptive stochastic methods in computational mathematics. The authors discuss the basic ideas of the algorithms and ways to analyze their properties and efficiency. Methods of evaluation of multidimensional integrals and solutions of integral equations are illustrated by multiple examples from mechanics, theory of elasticity, heat conduction and fluid dynamics. Contents Part I: Evaluation of Integrals Fundamentals of the Monte Carlo Method to Evaluate Definite Integrals Sequential Monte Carlo Method and Adaptive Integration Methods of Adaptive Integration Based on Piecewise Approximation Methods of Adaptive Integration Based on Global Approximation Numerical Experiments Adaptive Importance Sampling Method Based on Piecewise Constant Approximation Part II: Solution of Integral Equations Semi-Statistical Method of Solving Integral Equations Numerically Problem of Vibration Conductivity Problem on Ideal-Fluid Flow Around an Airfoil First Basic Problem of Elasticity Theory Second Basic Problem of Elasticity Theory Projectional and Statistical Method of Solving Integral Equations Numerically
Stochastic Adaptive Search For Global Optimization
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Author : Z.B. Zabinsky
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-09-30
Stochastic Adaptive Search For Global Optimization written by Z.B. Zabinsky 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 2003-09-30 with Computers categories.
The book overviews several stochastic adaptive search methods for global optimization and provides analytical results regarding their performance and complexity. It develops a class of hit-and-run algorithms that are theoretically motivated and do not require fine-tuning of parameters. Several engineering global optimization problems are summarized to demonstrate the kinds of practical problems that are now within reach. Audience: This book is suitable for graduate students, researchers and practitioners in operations research, engineering, and mathematics.
Adaptive Algorithms And Stochastic Approximations
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Author : Albert Benveniste
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Adaptive Algorithms And Stochastic Approximations written by Albert Benveniste 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 2012-12-06 with Mathematics categories.
Adaptive systems are widely encountered in many applications ranging through adaptive filtering and more generally adaptive signal processing, systems identification and adaptive control, to pattern recognition and machine intelligence: adaptation is now recognised as keystone of "intelligence" within computerised systems. These diverse areas echo the classes of models which conveniently describe each corresponding system. Thus although there can hardly be a "general theory of adaptive systems" encompassing both the modelling task and the design of the adaptation procedure, nevertheless, these diverse issues have a major common component: namely the use of adaptive algorithms, also known as stochastic approximations in the mathematical statistics literature, that is to say the adaptation procedure (once all modelling problems have been resolved). The juxtaposition of these two expressions in the title reflects the ambition of the authors to produce a reference work, both for engineers who use these adaptive algorithms and for probabilists or statisticians who would like to study stochastic approximations in terms of problems arising from real applications. Hence the book is organised in two parts, the first one user-oriented, and the second providing the mathematical foundations to support the practice described in the first part. The book covers the topcis of convergence, convergence rate, permanent adaptation and tracking, change detection, and is illustrated by various realistic applications originating from these areas of applications.
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.?
Adaptive Markov Control Processes
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Author : Onesimo Hernandez-Lerma
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Adaptive Markov Control Processes written by Onesimo Hernandez-Lerma 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 2012-12-06 with Mathematics categories.
This book is concerned with a class of discrete-time stochastic control processes known as controlled Markov processes (CMP's), also known as Markov decision processes or Markov dynamic programs. Starting in the mid-1950swith Richard Bellman, many contributions to CMP's have been made, and applications to engineering, statistics and operations research, among other areas, have also been developed. The purpose of this book is to present some recent developments on the theory of adaptive CMP's, i. e. , CMP's that depend on unknown parameters. Thus at each decision time, the controller or decision-maker must estimate the true parameter values, and then adapt the control actions to the estimated values. We do not intend to describe all aspects of stochastic adaptive control; rather, the selection of material reflects our own research interests. The prerequisite for this book is a knowledgeof real analysis and prob ability theory at the level of, say, Ash (1972) or Royden (1968), but no previous knowledge of control or decision processes is required. The pre sentation, on the other hand, is meant to beself-contained,in the sensethat whenever a result from analysisor probability is used, it is usually stated in full and references are supplied for further discussion, if necessary. Several appendices are provided for this purpose. The material is divided into six chapters. Chapter 1 contains the basic definitions about the stochastic control problems we are interested in; a brief description of some applications is also provided.
Stochastic Approximation Methods For Constrained And Unconstrained Systems
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Author : H.J. Kushner
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Stochastic Approximation Methods For Constrained And Unconstrained Systems written by H.J. Kushner 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 2012-12-06 with Mathematics categories.
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.
Intelligent Control
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Author : Kaushik Das Sharma
language : en
Publisher: Springer
Release Date : 2018-08-28
Intelligent Control written by Kaushik Das Sharma and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-28 with Technology & Engineering categories.
This book discusses systematic designs of stable adaptive fuzzy logic controllers employing hybridizations of Lyapunov strategy-based approaches/H∞ theory-based approaches and contemporary stochastic optimization techniques. The text demonstrates how candidate stochastic optimization techniques like Particle swarm optimization (PSO), harmony search (HS) algorithms, covariance matrix adaptation (CMA) etc. can be utilized in conjunction with the Lyapunov theory/H∞ theory to develop such hybrid control strategies. The goal of developing a series of such hybridization processes is to combine the strengths of both Lyapunov theory/H∞ theory-based local search methods and stochastic optimization-based global search methods, so as to attain superior control algorithms that can simultaneously achieve desired asymptotic performance and provide improved transient responses. The book also demonstrates how these intelligent adaptive control algorithms can be effectively utilized in real-life applications such as in temperature control for air heater systems with transportation delay, vision-based navigation of mobile robots, intelligent control of robot manipulators etc.
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.