Associative memories, stochastic activity networks and their application to sensor validation systems of nuclear power plants

Persistent Link:
http://hdl.handle.net/10150/278377
Title:
Associative memories, stochastic activity networks and their application to sensor validation systems of nuclear power plants
Author:
Shen, Bin, 1967-
Issue Date:
1993
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
In this paper, the problem of designing an advanced sensor validation system (SVS) which is robust and fault-tolerant under faulty conditions is considered. Associative memories, which provide robust pattern recognition are investigated as an information processing technology that can be applied to sensor validation. Studies of Binary Associative Memories (BAM) and Continuous Associative Memories (CAM) yield many results including (1) the stability condition of exemplars and spurious memories in BAMs, (2) the formula of choosing diagonal weights and bias that eliminates spurious memories most effectively in BAMs, (3) the convergence theory of CAMs that have asymmetric weight matrix with non-zero diagonal elements and non-monotonically increasing activation functions, (4) the energy function that explores the convergence behavior of CAMs, and (5) the hybrid learning algorithm that reduces spurious memories effectively in CAMs. The concept of performability is introduced to the evaluation of SVS. A set of important performability variables is introduced. Stochastic Activity Networks are used as a modeling tool to evaluate the performability of SVS. An illustration example, the evaluation of the pressurizer SVS of a PWR, is provided.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Engineering, Nuclear.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Nuclear and energy engineering
Degree Grantor:
University of Arizona
Advisor:
Williams, John G.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAssociative memories, stochastic activity networks and their application to sensor validation systems of nuclear power plantsen_US
dc.creatorShen, Bin, 1967-en_US
dc.contributor.authorShen, Bin, 1967-en_US
dc.date.issued1993en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractIn this paper, the problem of designing an advanced sensor validation system (SVS) which is robust and fault-tolerant under faulty conditions is considered. Associative memories, which provide robust pattern recognition are investigated as an information processing technology that can be applied to sensor validation. Studies of Binary Associative Memories (BAM) and Continuous Associative Memories (CAM) yield many results including (1) the stability condition of exemplars and spurious memories in BAMs, (2) the formula of choosing diagonal weights and bias that eliminates spurious memories most effectively in BAMs, (3) the convergence theory of CAMs that have asymmetric weight matrix with non-zero diagonal elements and non-monotonically increasing activation functions, (4) the energy function that explores the convergence behavior of CAMs, and (5) the hybrid learning algorithm that reduces spurious memories effectively in CAMs. The concept of performability is introduced to the evaluation of SVS. A set of important performability variables is introduced. Stochastic Activity Networks are used as a modeling tool to evaluate the performability of SVS. An illustration example, the evaluation of the pressurizer SVS of a PWR, is provided.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectEngineering, Nuclear.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineNuclear and energy engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorWilliams, John G.en_US
dc.identifier.proquest1356788en_US
dc.identifier.bibrecord.b31441300en_US
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