Learning Automata And Stochastic Optimization
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Learning Automata And Stochastic Optimization
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Author : A.S. Poznyak
language : en
Publisher: Springer
Release Date : 1997-03-12
Learning Automata And Stochastic Optimization written by A.S. Poznyak and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-03-12 with Computers categories.
In the last decade there has been a steadily growing need for and interest in computational methods for solving stochastic optimization problems with or wihout constraints. Optimization techniques have been gaining greater acceptance in many industrial applications, and learning systems have made a significant impact on engineering problems in many areas, including modelling, control, optimization, pattern recognition, signal processing and diagnosis. Learning automata have an advantage over other methods in being applicable across a wide range of functions. Featuring new and efficient learning techniques for stochastic optimization, and with examples illustrating the practical application of these techniques, this volume will be of benefit to practicing control engineers and to graduate students taking courses in optimization, control theory or statistics.
Networks Of Learning Automata
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Author : M.A.L. Thathachar
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-27
Networks Of Learning Automata written by M.A.L. Thathachar 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 2011-06-27 with Science categories.
Networks of Learning Automata: Techniques for Online Stochastic Optimization is a comprehensive account of learning automata models with emphasis on multiautomata systems. It considers synthesis of complex learning structures from simple building blocks and uses stochastic algorithms for refining probabilities of selecting actions. Mathematical analysis of the behavior of games and feedforward networks is provided. Algorithms considered here can be used for online optimization of systems based on noisy measurements of performance index. Also, algorithms that assure convergence to the global optimum are presented. Parallel operation of automata systems for improving speed of convergence is described. The authors also include extensive discussion of how learning automata solutions can be constructed in a variety of applications.
Learning Automata And Their Applications To Intelligent Systems
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Author : JunQi Zhang
language : en
Publisher: John Wiley & Sons
Release Date : 2023-12-12
Learning Automata And Their Applications To Intelligent Systems written by JunQi Zhang and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-12 with Technology & Engineering categories.
Comprehensive guide on learning automata, introducing two variants to accelerate convergence and computational update speed Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively; these two examples demonstrate how to design new learning automata for a specific field from the aspect of algorithm design to give full play to the advantage of learning automata. As noisy optimization problems exist widely in various intelligent systems, this book elaborates on how to employ learning automata to solve noisy optimization problems from the perspective of algorithm design and application. The existing and most representative applications of learning automata include classification, clustering, game, knapsack, network, optimization, ranking, and scheduling. They are well-discussed. Future research directions to promote an intelligent system are suggested. Written by two highly qualified academics with significant experience in the field, Learning Automata and Their Applications to Intelligent Systems covers such topics as: Mathematical analysis of the behavior of learning automata, along with suitable learning algorithms Two application-oriented learning automata: one to discover and track spatiotemporal event patterns, and the other to solve stochastic searching on a line Demonstrations of two pioneering variants of Optimal Computing Budge Allocation (OCBA) methods and how to combine learning automata with ordinal optimization How to achieve significantly faster convergence and higher accuracy than classical pursuit schemes via lower computational complexity of updating the state probability A timely text in a rapidly developing field, Learning Automata and Their Applications to Intelligent Systems is an essential resource for researchers in machine learning, engineering, operation, and management. The book is also highly suitable for graduate level courses on machine learning, soft computing, reinforcement learning and stochastic optimization.
Learning Automata
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Author : K. Najim
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Learning Automata written by K. Najim and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Technology & Engineering categories.
Learning systems have made a significant impact on all areas of engineering problems. They are attractive methods for solving many problems which are too complex, highly non-linear, uncertain, incomplete or non-stationary, and have subtle and interactive exchanges with the environment where they operate. The main aim of the book is to give a systematic treatment of learning automata and to produce a guide to a wide variety of ideas and methods that can be used in learning systems, including enough theoretical material to enable the user of the relevant techniques and concepts to understand why and how they can be used. The book also contains the materials that are necessary for the understanding and development of learning automata for different purposes such as processes identification, optimization and control. Learning Automata: Theory and Applications may be recommended as a reference for courses on learning automata, modelling, control and optimization. The presentation is intended both for graduate students in control theory and statistics and for practising control engineers.
Learning Automata
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Author : Kumpati S. Narendra
language : en
Publisher: Courier Corporation
Release Date : 2013-05-27
Learning Automata written by Kumpati S. Narendra and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-27 with Technology & Engineering categories.
This self-contained introductory text on the behavior of learning automata focuses on how a sequential decision-maker with a finite number of choices responds in a random environment. Topics include fixed structure automata, variable structure stochastic automata, convergence, 0 and S models, nonstationary environments, interconnected automata and games, and applications of learning automata. A must for all students of stochastic algorithms, this treatment is the work of two well-known scientists and is suitable for a one-semester graduate course in automata theory and stochastic algorithms. This volume also provides a fine guide for independent study and a reference for students and professionals in operations research, computer science, artificial intelligence, and robotics. The authors have provided a new preface for this edition.
Advances In Learning Automata And Intelligent Optimization
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Author : Javidan Kazemi Kordestani
language : en
Publisher: Springer Nature
Release Date : 2021-06-23
Advances In Learning Automata And Intelligent Optimization written by Javidan Kazemi Kordestani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-23 with Technology & Engineering categories.
This book is devoted to the leading research in applying learning automaton (LA) and heuristics for solving benchmark and real-world optimization problems. The ever-increasing application of the LA as a promising reinforcement learning technique in artificial intelligence makes it necessary to provide scholars, scientists, and engineers with a practical discussion on LA solutions for optimization. The book starts with a brief introduction to LA models for optimization. Afterward, the research areas related to LA and optimization are addressed as bibliometric network analysis. Then, LA's application in behavior control in evolutionary computation, and memetic models of object migration automata and cellular learning automata for solving NP hard problems are considered. Next, an overview of multi-population methods for DOPs, LA's application in dynamic optimization problems (DOPs), and the function evaluation management in evolutionary multi-population for DOPs are discussed. Highlighted benefits • Presents the latest advances in learning automata-based optimization approaches. • Addresses the memetic models of learning automata for solving NP-hard problems. • Discusses the application of learning automata for behavior control in evolutionary computation in detail. • Gives the fundamental principles and analyses of the different concepts associated with multi-population methods for dynamic optimization problems.
Reinforcement Learning And Stochastic Optimization
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Author : Warren B. Powell
language : en
Publisher: John Wiley & Sons
Release Date : 2022-03-15
Reinforcement Learning And Stochastic Optimization written by Warren B. Powell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-15 with Mathematics categories.
REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
First Order And Stochastic Optimization Methods For Machine Learning
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Author : Guanghui Lan
language : en
Publisher: Springer Nature
Release Date : 2020-05-15
First Order And Stochastic Optimization Methods For Machine Learning written by Guanghui Lan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-15 with Mathematics categories.
This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
Stochastic Processes
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Author : Kaddour Najim
language : en
Publisher: Elsevier
Release Date : 2004-07-01
Stochastic Processes written by Kaddour Najim and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-07-01 with Mathematics categories.
A ‘stochastic’ process is a ‘random’ or ‘conjectural’ process, and this book is concerned with applied probability and statistics. Whilst maintaining the mathematical rigour this subject requires, it addresses topics of interest to engineers, such as problems in modelling, control, reliability maintenance, data analysis and engineering involvement with insurance.This book deals with the tools and techniques used in the stochastic process – estimation, optimisation and recursive logarithms – in a form accessible to engineers and which can also be applied to Matlab. Amongst the themes covered in the chapters are mathematical expectation arising from increasing information patterns, the estimation of probability distribution, the treatment of distribution of real random phenomena (in engineering, economics, biology and medicine etc), and expectation maximisation. The latter part of the book considers optimization algorithms, which can be used, for example, to help in the better utilization of resources, and stochastic approximation algorithms, which can provide prototype models in many practical applications. * An engineering approach to applied probabilities and statistics * Presents examples related to practical engineering applications, such as reliability, randomness and use of resources* Readers with varying interests and mathematical backgrounds will find this book accessible
Recent Advances In Learning Automata
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Author : Alireza Rezvanian
language : en
Publisher: Springer
Release Date : 2018-01-17
Recent Advances In Learning Automata written by Alireza Rezvanian and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-17 with Technology & Engineering categories.
This book collects recent theoretical advances and concrete applications of learning automata (LAs) in various areas of computer science, presenting a broad treatment of the computer science field in a survey style. Learning automata (LAs) have proven to be effective decision-making agents, especially within unknown stochastic environments. The book starts with a brief explanation of LAs and their baseline variations. It subsequently introduces readers to a number of recently developed, complex structures used to supplement LAs, and describes their steady-state behaviors. These complex structures have been developed because, by design, LAs are simple units used to perform simple tasks; their full potential can only be tapped when several interconnected LAs cooperate to produce a group synergy. In turn, the next part of the book highlights a range of LA-based applications in diverse computer science domains, from wireless sensor networks, to peer-to-peer networks, to complex social networks, and finally to Petri nets. The book accompanies the reader on a comprehensive journey, starting from basic concepts, continuing to recent theoretical findings, and ending in the applications of LAs in problems from numerous research domains. As such, the book offers a valuable resource for all computer engineers, scientists, and students, especially those whose work involves the reinforcement learning and artificial intelligence domains.