Applicability of Learning Automata Theory to Neural Network Design
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
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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.
{'FirstName': 'Kumpati', 'LastName': 'Narendra', 'PI_MID_INIT': 'S', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Kumpati S Narendra', 'EmailAddress': 'kumpati.narendra@yale.edu', 'NSF_ID': '000090413', 'StartDate': '06/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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[{'Code': '145400', 'Text': 'SYSTEMS THEORY'}, {'Code': '149400', 'Text': 'NEUROENGINEERING'}]
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