Research Initiation: Artificial Neural Network ArchitecturesBased on High-Performance Associative Neural Memories (ANMs)
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Paul Werbos', 'PO_EMAI': None, 'PO_PHON': None}
This project will study the capabilities of a new class of associative          neural memories (ANM) developed by the P.I. in prior work.  These               systems show promise of expanded memory capacity and acceptable                 convergence.  In particular, one system, borrowing concepts from the            Ho-Kashyap algorithm for pattern classification, has outperformed the           standard Hopfield and Kohonen ANM's in preliminary tests.                                                                                                       This project will institute a more comprehensive series of tests,               including diagnostic tests which will be used to guide the design of a          more powerful ANM.  In developing new designs, the P.I. will pay                special attention to the similarity measures used in the various                algorithms, to multilayer designs, and to applications such as digital          multiplication which require high network capacity.
{'FirstName': 'Mohamad', 'LastName': 'Hassoun', 'PI_MID_INIT': 'H', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Mohamad H Hassoun', 'EmailAddress': 'hassoun@eng.wayne.edu', 'NSF_ID': '000330725', 'StartDate': '08/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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[{'Code': '1494', 'Text': 'NEUROENGINEERING'}, {'Code': '9120', 'Text': 'CROSS-DIRECTORATE PROGRAMS'}]
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