Staff View
A generalized hybrid fuzzy-Bayesian methodology for modeling complex uncertainty

Descriptive

TypeOfResource
Text
TitleInfo (ID = T-1)
Title
A generalized hybrid fuzzy-Bayesian methodology for modeling complex uncertainty
SubTitle
PartName
PartNumber
NonSort
Identifier (displayLabel = ); (invalid = )
ETD_1995
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051885
Language (objectPart = )
LanguageTerm (authority = ISO639-2); (type = code)
eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (ID = SBJ-2); (authority = ETD-LCSH)
Topic
Bayesian statistical decision theory
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
Mathematical models
Abstract
Due to its well understood nature and its ability to model many phenomena in the physical world extremely well, probability theory is the method of choice for dealing with uncertainty in many science and engineering disciplines. However, as a tool for building representative models of complex real world systems, probability theory has a rather recent history which starts with the introduction of Bayesian Networks (BN).
Broadly construed, the BN model of a system is the compact representation of a joint probability distribution of the variables comprising the system. Many complex real-world systems are naturally represented by hybrid models which contain both discrete and continuous variables. However, when it comes to modeling uncertainty and to performing probabilistic inferencing about hybrid systems, what BNs have to offer is quite limited. Although exact inferencing in BNs composed only of discrete variables is well understood, no exact inferencing algorithms exist for general hybrid BNs.
In this thesis we concentrate on the problem of inferencing in Hybrid Bayesian Networks (HBNs). Our focus, hence our contributions are three-fold: theoretical, algorithmic and practical. From a theoretical point of view, we provide a novel framework to implement a hybrid methodology that complements probability theory with Fuzzy Sets to perform exact inferencing with general Hybrid Bayesian Networks that is composed of both discrete and continuous variables with no graph-structural restrictions to model uncertainty in complex systems. From an algorithmic perspective, we provide a suite of inferencing algorithms for general Hybrid Bayesian Networks. The suite includes two new inferencing algorithms for the two different types of Fuzzy-Bayesian Networks introduced in this study. Finally, from a practical perspective, we apply our framework, methodology, and techniques to the task of assessing system safety risk due to the introduction of emergent Unmanned Aircraft Systems into the National Airspace System.
PhysicalDescription
Form (authority = gmd)
electronic resource
Extent
xiii, 195 p. : ill.
InternetMediaType
application/pdf
InternetMediaType
text/xml
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 188-193)
Note (type = statement of responsibility)
by Ahmet Öztekin
Name (ID = NAME-1); (type = personal)
NamePart (type = family)
Öztekin
NamePart (type = given)
Ahmet
Role
RoleTerm (authority = RULIB); (type = )
author
DisplayForm
Ahmet Öztekin
Name (ID = NAME-2); (type = personal)
NamePart (type = family)
Luxhoj
NamePart (type = given)
James
Role
RoleTerm (authority = RULIB); (type = )
chair
Affiliation
Advisory Committee
DisplayForm
James T Luxhoj
Name (ID = NAME-3); (type = personal)
NamePart (type = family)
Boucher
NamePart (type = given)
Thomas
Role
RoleTerm (authority = RULIB); (type = )
internal member
Affiliation
Advisory Committee
DisplayForm
Thomas O Boucher
Name (ID = NAME-4); (type = personal)
NamePart (type = family)
Coit
NamePart (type = given)
David
Role
RoleTerm (authority = RULIB); (type = )
internal member
Affiliation
Advisory Committee
DisplayForm
David W Coit
Name (ID = NAME-5); (type = personal)
NamePart (type = family)
Lawrence
NamePart (type = given)
Sheila
Role
RoleTerm (authority = RULIB); (type = )
outside member
Affiliation
Advisory Committee
DisplayForm
Sheila M Lawrence
Name (ID = NAME-1); (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB); (type = )
degree grantor
Name (ID = NAME-2); (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB); (type = )
school
OriginInfo
DateCreated (point = ); (qualifier = exact)
2009
DateOther (qualifier = exact); (type = degree)
2009-10
Place
PlaceTerm (type = code)
xx
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3NK3F62
Genre (authority = ExL-Esploro)
ETD doctoral
Back to the top

Rights

RightsDeclaration (AUTHORITY = GS); (ID = rulibRdec0006)
The author owns the copyright to this work
Copyright
Status
Copyright protected
Notice
Note
Availability
Status
Open
Reason
Permission or license
Note
RightsHolder (ID = PRH-1); (type = personal)
Name
FamilyName
Öztekin
GivenName
Ahmet
Role
Copyright holder
RightsEvent (ID = RE-1); (AUTHORITY = rulib)
Type
Permission or license
Label
Place
DateTime
Detail
AssociatedEntity (ID = AE-1); (AUTHORITY = rulib)
Role
Copyright holder
Name
Ahmet Öztekin
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject (ID = AO-1); (AUTHORITY = rulib)
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Back to the top

Technical

ContentModel
ETD
MimeType (TYPE = file)
application/pdf
MimeType (TYPE = container)
application/x-tar
FileSize (UNIT = bytes)
2611200
Checksum (METHOD = SHA1)
8041396b893a9e4bdcb6e996ea406357bd655491
Back to the top
Version 8.4.8
Rutgers University Libraries - Copyright ©2022