Learning Automata : Theory and Applications

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 Content: Related Pergamon Titles, Page ii Front Matter, Page iii Copyright, Page iv Preface, Pages ix-xi Notations, Pages 1-2 Introduction, Pages 3-5 1 - Basic Notions and Definitions, Pages 6-18 2 - Reinforcement Schemes for Average Loss Function Minimization, Pages 19-39 3 - Behaviour of Learning Automata for Different Reinforcement Schemes, Pages 40-76 4 - Multilevel Systems of Automata, Pages 77-119 5 - Multimodal Function Optimization Using Learning Automata, Pages 120-143 6 - Applications of Learning Automata, Pages 144-205 References, Pages 206-214 APPENDIX, Pages 215-223 Index, Pages 224-225 This text on learning automata aims 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.