Masters Theses

Ritesh Arora

Abstract

"This research highlights the use of moment closure approximation techniques to provide analytical approximations to nonlinear state dependent systems. Nonlinear systems are often difficult to solve and often approximated through the use of simulation. However, Moment Closure has the capability to derive an approximate solution by making a closed form solution out of open set of differential equations representing nonlinear system behavior in both transient and equilibrium state. It is this capability of approximating the closed form solution to complex systems that draws interest in the use of this method within systems engineering. This research further extends to the stability analysis of the derived differential equations using Jacobian Matrix and Eigen Value method. We demonstrate the effectiveness of the method through various examples. We address the issue of using underlying parametric assumptions for closing the open set of differential equations for large systems, which are typically hard to solve. In this research we seek to show that it is better to have a distributional assumption then just having no assumption and neglecting the cumulants arbitrarily. We also show that how this method can be applied to systems of systems with little or no interdependence between the systems, assuming that each and every system can be represented by a single stochastic variable"--Abstract, page iii.

Advisor(s)

Guardiola, Ivan

Committee Member(s)

Adekpedjou, Akim
Dagli, Cihan H., 1949-

Department(s)

Engineering Management and Systems Engineering

Degree Name

M.S. in Engineering Management

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2012

Pagination

viii, 41 pages

Note about bibliography

Includes bibliographical references (pages 43-45).

Rights

© 2012 Ritesh Arora, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Nonlinear systems -- Mathematical modelsStability -- AnalysisStochastic processes

Thesis Number

T 9957

Print OCLC #

815789183

Electronic OCLC #

815789220

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