Learning automata : theory and applications
著者
書誌事項
Learning automata : theory and applications
Pergamon : Elsevier Science, c1994
大学図書館所蔵 件 / 全10件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Bibliography: 206-214
Includes index
内容説明・目次
内容説明
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 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 enable the user of the relevent techniques and concepts to understand why and how they can be used. This book contains the materials that are necessary for the understanding and development of learning automata for different purposes as processes identification, optimization and control. This book may be recommended as a reference for courses on learning automata, modelling, control and optimization. This presentation is intended both for graduate students in control theory, statistics and for practicing control engineers AUDIENCE For graduate students in control theory and statistics and for practicing control engineers
目次
Preface. Contents. Notations. Introduction. Basic notations and definitions. Introduction. Contolled finite system. Control strategies. Dynamic characteristics of controlled finite systems and their structures. Adaptive strategies and learning automata. Classification of problems of adaptive control of finite systems. Reinforcement schemes for average loss function minimization. Introduction. Adaptive control and static systems and linear programming problem. Reinforcement schemes. Properties of reinforcement schemes Behaviour of learning automata for different Reinforcement Schemes. Introduction Reinforcement scheme of Narendra-Shapiro. Reinforcement scheme of Luce and Varshavskii-Verontsova. Bush-Mosteller reinforcement scheme. Projectional stochastic approximation algorithm. Conclusion. Multilevel systems for Automata. Introduction Hierarchical system .The connection between two level adaptive control and bilinear programming problem. Two-level hierarchical system of learning automata using a projectional stochastic approximation algorithm. Two level hierarchical system with transmission of current information to the lower level. Multilevel hierarchical learning system. Conclusion. Multimodal function optimization using learning automata. Introduction. Optimization using single learning automata. Optimization using a two level hierarchical system of learning automata. Conclusion. Application of Learning Automata. Introduction. Practical aspects. Multilevel learning control of a drying furnace. Hierarchical learning control of absorption column. Learning control of an evaporator. Adaptive choice of cyclic code in communications systems. Optimization of multimodal functions ( without constraints). Optimization in presence of constraints. Application of learning automaton to neural network synthesis. Conclusion. Nomenclature. References. Appendix. Index
「Nielsen BookData」 より
