Model Reduction and Stochastic Realizations
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Radhakisan Baheti', 'PO_EMAI': None, 'PO_PHON': None}
This research concerns modeling of stochastic systems.  For these               systems, the work will be directed towards the development of an                infinite-dimensional realization theory, the solution of problems in            model order reduction, and the development of a bilinear system theory.         Current work on linear stochastic infinite-dimensional realization              theory requires certain restrictive assumptions to find the minimal             state space realization of any given stationary Gaussian random                 process.  Thus, the first objective is to relax these assumptions to            obtain a theory applicable to a wider range of processes.  The work on          model reduction concerns the finding of a relatively low order model of         a high order system.  This has important implications for on-line               control where the control action must be calculated in real time.  The          work on bilinear systems, a relatively simple form of nonlinear                 stochastic model, will attempt to extend the work on linear systems to          this class of models.  The approach to be used in this research is              based on certain aspects of functional analysis and the geometry of             random processes.
{'FirstName': 'Arthur', 'LastName': 'Frazho', 'PI_MID_INIT': 'E', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Arthur E Frazho', 'EmailAddress': 'arthur.e.frazho.1@purdue.edu', 'NSF_ID': '000329447', 'StartDate': '04/01/1985', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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[{'Code': '1262', 'Text': 'MODERN ANALYSIS'}, {'Code': '1266', 'Text': 'APPLIED MATHEMATICS'}, {'Code': '1454', 'Text': 'SYSTEMS THEORY'}]
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