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Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 195-199).

Thesis (M.S.)--Rochester Institute of Technology, 1994. / Typescript. Includes bibliographical references (leaf 123).

A new approach to the identification problem of linear continuous-time time-invariant systems from input-output measurements is presented. Both parametric and nonparametric system models are considered. The new approach is based on the use of continuous-time functions, the modal functions, defined in terms of the system output, the output derivatives and the state variables under the assumption that the order n of the observable system is known a priori. The modal functions are obtained by linear filtering operations of the system output, the output derivatives and the state variables so that the modal functions are independent of the system instantaneous state. In this case, the modal functions are linear functions of the input exponential modes, and they contain none of the system exponential modes unlike the system general response which contains modes from both the system and the input. The filters parameters, the modal parameters are estimated using linear regression techniques. The modal functions and the modal parameters of the output and its derivatives are used to identify parametric input-output and state models of the system. The coefficients of the system characteristic polynomial are obtained by solving n algebraic equations formed from the estimates of the modal parameters. Estimates of the parameters associated with the system zeros are obtained by solving another set of linear algebraic equation. The system frequency response and step response are estimated using the output modal function. The impulse response is obtained by filtering the estimated step response using the output first derivative modal parameters. A new method is presented to obtain the system poles as the eigenvalues of a data matrix formed from the system free response. The coefficients of the system characteristic polynomial are obtained from the data matrix through a simple recursive equation. This method has some important advantages over the well known Prony's method. The state modal functions are used to obtain a minimum-time observer that gives the continuous-time system state as a direct function of input-output samples in n sampling intervals. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

A systematic optimization of the Cohen class time-frequency transformation for detecting the parameters change is developed. The local moments approach to change detection is proposed and a general formula for the local moments is derived. The optimal kernel functions of the time-frequency transformation are determined based on the combined criteria of maximum sensitivity with respect to parameters change and minimum distortion of physical interpretation of the local moments. The sensitivity of the local moment with respect to a certain kind of inputs is analyzed and a most "convenient" and a "worst" input are identified. The results are presented in the form of the case studies for detecting parameters change in simple linear systems. / Graduation date: 1992

Thesis (M.S.)--Rochester Institute of Technology, 1990. / Spine title: Model reduction of complex dynamic systems. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 1994. / Typescript. Includes bibliographical references (leaves 107-108).

Xi-Cheng Lou ... [et al.]. / Bibliography: leaf 14. / "October, 1983." / Air Force Office of Scientific Research Contract AFOSR-82-0258 Natural Sciences and Engineering Research Council of Canada Grant A-1240

by John L. Wyatt, Jr., William M. Siebert, Han-Ngee Tan. / "October, 1982." / Bibliography: p. 16-17. / National Science Foundation Grant No. ECS 800 6878

This thesis examines several problems related to singly-constrained Monotropic Network Flow Problems. In the first part, a linear time algorithm that reduces the solution of a monotropic network flow problem with an additional linear equality constraint to the solution of lower dimensional subproblems is presented. Of the subproblems, at most one is a singly-constrained monotropic network flow problem while the others are unconstrained. If none of the subproblems is constrained, the algorithm provides a linear-time transformation of constrained to unconstrained monotropic network flow problems. Extensions to nonlinear and inequality constraints are given. In the second part the qualitative theory of sensitivity analysis for Unconstrained Minimum-Cost Flow Problems presented by Granot and Veinott [GV85] is extended to Minimum-Cost Flow Problems with one additional linear constraint. The departure from the unconstrained network structure is shown to have a profound effect on computational issues. Two natural extensions of the "less-dependent-on" partial ordering of the arcs given in [GV85] are presented. One is decidable in linear time while the other yields more information but is NP-complete in general. The Ripple Theorem gives upper bounds on the absolute value of optimal-flow variations as a function of variations in the problem parameter. Moreover, it shows how changes may "ripple down" throughout the network, decreasing in magnitude as one gets "further away" from the arc whose parameter initiated the change. The Theory of Substitutes and Complements presents necessary and sufficient conditions for optimal-flow changes to consistently have the same (or the opposite) sign in two given arcs. The complexity of determining Substitutes and Complements is shown to be NP-complete in general. However, for all intractable problems, families of cases arise from easily recognizable graph structures and can be computed in linear time. The Monotonicity Theory links the changes in the value of the parameters to the change in the optimal arc-flows. Bounds on the rates of changes are discussed. We further provide a number of practical situations where our theory may apply. We discuss some Multi-Period Multi-Product Inventory-Production models that can be formulated as nonlinear parametric network flow problems with one additional linear constraint. We then apply our theory to help decision makers understand qualitatively how to respond to changes in the environment such as machine breakdown, strike or variations in inventory carrying costs without additional computation. In a second example, we show how a Cash-Flow Management model can be formulated as a nonlinear parametric network flow problem with one additional linear constraint. The theory is then recommended as a method by which a decision maker could understand qualitatively how to respond to changes in the environment such as variations in interest rates, taxes or asset prices without any additional computation. / Business, Sauder School of / Graduate