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Neural Networks Based Approach to Enhance Space Hardware ReliabilityThis paper demonstrates the use of Neural Networks as a device modeling tool to increase the reliability analysis accuracy of circuits targeted for space applications. The paper tackles a number of case studies of relevance to the design of Flight hardware. The results show that the proposed technique generates more accurate models than the ones regularly used to model circuits.
Document ID
20150005767
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Authors
Zebulum, Ricardo S.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Thakoor, Anilkumar
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Lu, Thomas
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Franco, Lauro
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Lin, Tsung Han
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
McClure, S. S.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
April 16, 2015
Publication Date
August 4, 2011
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Meeting Information
Meeting: International Society of Science and Applied Technologies (ISSAT) International Conference Reliability and Quality in Design
Location: Vancouver, B.C.
Country: Canada
Start Date: August 4, 2011
End Date: August 6, 2011
Sponsors: International Society of Science and Applied Technologies
Distribution Limits
Public
Copyright
Other
Keywords
Neural Networks
device modeling

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