Simulated Annealing and Neurla Networks for Large-Scale     Optimization Problems
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Kristen M. Biggar, N-BioS', 'PO_EMAI': None, 'PO_PHON': None}
Control strategies for many large-scale engineering applications are            often formulated as constrained optimization problems as exemplified            by the optimal power flow (OPF) formulation for power system security.          At present, only simplistic models of the OPF, using d.c. load flows,           can be solved in a time-frame fast enough for real-time control.  When          reactive power and voltage along with more complex constraints are              considered, solving the OPF problem becomes too time consuming.  The            goal of this project is to enhance the computational throughput of OPF          and similar problems by applying two new approaches to constrained              optimization which can how be implemented efficiently using nascent             ASIC (application-specific integrated circuit) technology.  These new           optimization approaches are the simulated annealing algorithm                   (Astochastic state-space search algorithm) and the computational                concept of neural networks (massively parallel, redundant,                      computational structures which are very conducive to VLSI                       implementation.)  The project objectives are to first find a strategy           for mapping the OPF and similar problems into the energy and                    constraint functions for simulated annealing and neural networks.               Then, simulations for extracting the performance and cost benefits of           these approaches will be developed.  Finally, implementation of these           systems in dedicated hardware using the latest ASIC technology will be          assessed.
{'FirstName': 'Michael', 'LastName': 'Shanblatt', 'PI_MID_INIT': 'A', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Michael A Shanblatt', 'EmailAddress': 'mas@egr.msu.edu', 'NSF_ID': '000215213', 'StartDate': '09/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Michigan State University', 'CityName': 'EAST LANSING', 'ZipCode': '488242600', 'PhoneNumber': '5173555040', 'StreetAddress': '426 AUDITORIUM RD RM 2', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Michigan', 'StateCode': 'MI', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'MI07', 'ORG_UEI_NUM': 'R28EKN92ZTZ9', 'ORG_LGL_BUS_NAME': 'MICHIGAN STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'VJKZC4D1JN36'}
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[{'Code': '1495', 'Text': 'COMPUTATIONAL ENGINEERING'}, {'Code': '1518', 'Text': 'CONTROL, NETWORKS, & COMP INTE'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8814035.xml'}