Applicability of Learning Automata Theory to Neural Network Design

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This is a preliminary research effort to determine whether powerful tools and concepts of learning automata theory can be brought to bear on a variety of optimization problems which arise in neural networks. In the first phase of the proposed research a detailed study of several simple interconnected automata will be undertaken. The primary aim of this is to determine how known results can be extended to generalized automata and hence to neural networks. In the second phase the emphasis will be on the use of learning automata methods of optimization of general hierarchical systems of which the vision system described earlier is a typical example. Computer simulations of both simple and complex networks are contemplated during both phases of the research. Observation of interesting simulated behavior will provide both sources of new theoretical focus as well as clues to new results.