Persistent Link:
http://hdl.handle.net/10150/288893
Title:
Measure-adaptive state-space construction methods
Author:
Obal, Walter Douglas, 1966-
Issue Date:
1998
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:
Much work has been done on the problem of stochastic modeling for the evaluation of performance, dependability and performability properties of systems, but little attention has been given to the interplay between the model and the performance measure of interest. Our work addresses the problem of automatically constructing Markov processes tailored to the structure of the system and the nature of the performance measures of interest. To solve this problem, we have developed new techniques for detecting and exploiting symmetry in the model structure, new reward variable specification techniques, and new state-space construction procedures. We propose a new method for detecting and exploiting model symmetry in which (1) models retain the structure of the system, and (2) all symmetry inherent in the structure of the model can be detected and exploited for the purposes of state-space reduction. Then, we extend the array of performance measures that may be derived from a given system model by introducing a class of path-based reward variables, which allow rewards to be accumulated based on sequences of states and transitions. Finally, we describe a new reward variable specification formalism and state-space construction procedure for automatically computing the appropriate level of state-space reduction based on the nature of the reward variables and the structural symmetry in the system model.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical and Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Marcellin, Michael W.; Sanders, William H.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleMeasure-adaptive state-space construction methodsen_US
dc.creatorObal, Walter Douglas, 1966-en_US
dc.contributor.authorObal, Walter Douglas, 1966-en_US
dc.date.issued1998en_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.abstractMuch work has been done on the problem of stochastic modeling for the evaluation of performance, dependability and performability properties of systems, but little attention has been given to the interplay between the model and the performance measure of interest. Our work addresses the problem of automatically constructing Markov processes tailored to the structure of the system and the nature of the performance measures of interest. To solve this problem, we have developed new techniques for detecting and exploiting symmetry in the model structure, new reward variable specification techniques, and new state-space construction procedures. We propose a new method for detecting and exploiting model symmetry in which (1) models retain the structure of the system, and (2) all symmetry inherent in the structure of the model can be detected and exploited for the purposes of state-space reduction. Then, we extend the array of performance measures that may be derived from a given system model by introducing a class of path-based reward variables, which allow rewards to be accumulated based on sequences of states and transitions. Finally, we describe a new reward variable specification formalism and state-space construction procedure for automatically computing the appropriate level of state-space reduction based on the nature of the reward variables and the structural symmetry in the system model.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorMarcellin, Michael W.en_US
dc.contributor.advisorSanders, William H.en_US
dc.identifier.proquest9906511en_US
dc.identifier.bibrecord.b38862414en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.