Learning Automata

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