Stochastic Automata: Stability, Nondeterminism and Prediction PDF Download

Author: E.-E. Doberkat
Publisher: Springer Science & Business Media
ISBN: 9783540108351
Category : Mathematics
Languages : en
Pages : 152

View

Book Description

Author: Ernst-Erich Doberkat
Publisher: Springer
ISBN:
Category : Mathematics
Languages : en
Pages : 1472

View

Book Description
Provides a general mathematical framework for the analytical aspects of stochastic automata. Shows that under certain topological conditions, non-deterministic automata are generated, which in some cases are produced by stochastic automata.

Author: Pedro U. Lima
Publisher: World Scientific
ISBN: 9789810222550
Category : Computers
Languages : en
Pages : 172

View

Book Description
In recent years works done by most researchers towards building autonomous intelligent controllers frequently mention the need for a methodology of design and a measure of how successful the final result is. This monograph introduces a design methodology for intelligent controllers based on the analytic theory of intelligent machines introduced by Saridis in the 1970s. The methodology relies on the existing knowledge about designing the different sub-systems composing an intelligent machine. Its goal is to provide a performance measure applicable to any of the sub-systems, and use that measure to learn on-line the best among the set of pre-designed alternatives, given the state of the environment where the machine operates. Different designs can be compared using this novel approach.

Author: William J. Stewart
Publisher: Princeton University Press
ISBN: 0691036993
Category : Mathematics
Languages : en
Pages : 561

View

Book Description
Markov Chains -- Direct Methods -- Iterative Methods -- Projection Methods -- Block Hessenberg Matrices -- Decompositional Methods -- LI-Cyclic Markov -- Chains -- Transient Solutions -- Stochastic Automata Networks -- Software.

Author: M.A.L. Thathachar
Publisher: Springer Science & Business Media
ISBN: 1441990526
Category : Science
Languages : en
Pages : 275

View

Book Description
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.

Author: Aivar Arvidovich Lorents
Publisher: John Wiley & Sons
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 194

View

Book Description

Author: Kumpati S. Narendra
Publisher: Courier Corporation
ISBN: 0486268462
Category : Technology & Engineering
Languages : en
Pages : 498

View

Book Description
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.

Author: Azaria Paz
Publisher: Academic Press
ISBN: 1483268578
Category : Mathematics
Languages : en
Pages : 255

View

Book Description
Introduction to Probabilistic Automata deals with stochastic sequential machines, Markov chains, events, languages, acceptors, and applications. The book describes mathematical models of stochastic sequential machines (SSMs), stochastic input-output relations, and their representation by SSMs. The text also investigates decision problems and minimization-of-states problems arising from concepts of equivalence and coverings for SSMs. The book presents the theory of nonhomogeneous Markov chains and systems in mathematical terms, particularly in relation to asymptotic behavior, composition (direct sum or product), and decomposition. "Word functions," induced by Markov chains and valued Markov systems, involve characterization, equivalence, and representability by an underlying Markov chain or system. The text also discusses the closure properties of probabilistic languages, events and their relation to regular events, particularly with reference to definite, quasidefinite, and exclusive events. Probabilistic automata theory has applications in information theory, control, learning theory, pattern recognition, and time sharing in computer programming. Programmers, computer engineers, computer instructors, and students of computer science will find the collection highly valuable.

Author: E. E. Doberkat
Publisher:
ISBN: 9783662165744
Category :
Languages : en
Pages : 152

View

Book Description

Author: A.S. Poznyak
Publisher: Springer
ISBN:
Category : Computers
Languages : en
Pages : 230

View

Book Description
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.