Includes summary. Bibliography: leaves 108-112.

Bibliography: leaves 146-152 / vi, 152 leaves ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Applied Mathematics, 1987

The design of a stochastic optimal controller using state feedback and output feedback is developed for unified, decentralized, singularly perturbed systems with Gaussian noise. To filter out the external noises contained in the system signals, a unified optimal observer (Kalman filter) is used for the decentralized, singularly perturbed system with a reduced-order model. The reduced-order stabilizing observer is also derived by the unified Riccati equation approach. Rationalization of the decentralized, singularly perturbed system with time delays is presented in the frequency domain by using the delta operator approach. It is shown that the discrete-time system is realized into the discrete-time state-space model. The stability robustness of a unified decentralized singularly perturbed stochastic system is investigated by exploring stability bounds under system uncertainties. A new unified stochastic bound is derived for both "unstructured" and "structured" time-varying independent perturbations. / Thesis (Ph.D.)--Wichita State University, College of Engineering. / Includes bibliographic references (leaves 128-132). / "May 2006." / Includes bibliographic references (leaves 128-132)

Thesis (Ph. D.)--Lehigh University, 2000. / Includes vita. Includes bibliographical references (leaves 125-135).

Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (Ph. D.)--Ohio State University, 1967. / Includes bibliographical references. Available online via OhioLINK's ETD Center

Thesis (Ph. D.)--Ohio State University, 1968. / Includes bibliographical references (leaves 76-77). Available online via OhioLINK's ETD Center

Thesis (M. Phil.)--University of Queensland, 2002. / Includes bibliographical references.

This research addresses three important questions for solving a general stochastic optimization problem: proper modeling of the uncertainties and their interactions, use of decomposition techniques to solve the resulting optimization problems, and the impact of stochastic dependencies to the optimal solution. In particular, we develop sampling methodologies for scenario generation that preserve the cointegration properties of financial time series, create a new conditional decision-dependent probability model for the lifetime of components in nuclear power plants, define the corresponding stochastic optimization problems, and construct decomposition algorithms to solve them. We investigate the impact of the input (in terms of different stochastic dependencies) to the solution of the corresponding optimization problem. For the last issue we concentrate on the general financial asset allocation problem. / text

No description available.