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| Author | : E.-E. Doberkat |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 152 |
| Release | : 1981-06 |
| Genre | : Mathematics |
| ISBN | : 9783540108351 |
| Author | : Ernst-Erich Doberkat |
| Publisher | : Springer |
| Total Pages | : 1472 |
| Release | : 1881 |
| Genre | : Mathematics |
| ISBN | : |
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 |
| Total Pages | : 172 |
| Release | : 1996 |
| Genre | : Computers |
| ISBN | : 9789810222550 |
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 | : M.A.L. Thathachar |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 275 |
| Release | : 2011-06-27 |
| Genre | : Science |
| ISBN | : 1441990526 |
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 | : William J. Stewart |
| Publisher | : Princeton University Press |
| Total Pages | : 561 |
| Release | : 1994-12-04 |
| Genre | : Mathematics |
| ISBN | : 0691036993 |
Markov Chains -- Direct Methods -- Iterative Methods -- Projection Methods -- Block Hessenberg Matrices -- Decompositional Methods -- LI-Cyclic Markov -- Chains -- Transient Solutions -- Stochastic Automata Networks -- Software.
| Author | : Azaria Paz |
| Publisher | : Academic Press |
| Total Pages | : 255 |
| Release | : 2014-05-10 |
| Genre | : Mathematics |
| ISBN | : 1483268578 |
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 | : Kumpati S. Narendra |
| Publisher | : Courier Corporation |
| Total Pages | : 498 |
| Release | : 2013-05-27 |
| Genre | : Technology & Engineering |
| ISBN | : 0486268462 |
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 | : E. E. Doberkat |
| Publisher | : |
| Total Pages | : 152 |
| Release | : 2014-01-15 |
| Genre | : |
| ISBN | : 9783662165744 |
| Author | : Aivar Arvidovich Lorents |
| Publisher | : John Wiley & Sons |
| Total Pages | : 194 |
| Release | : 1974 |
| Genre | : Technology & Engineering |
| ISBN | : |
| Author | : A.S. Poznyak |
| Publisher | : Springer |
| Total Pages | : 230 |
| Release | : 1997-03-12 |
| Genre | : Computers |
| ISBN | : |
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.
