Robust design-that is, managing design uncertainties such as model uncertainty or parametric uncertainty-is the often unpleasant issue crucial in much multidisciplinary optimal design work. Recently, there has been enormous practical interest in strategies for applying optimization tools to the development of robust solutions and designs in several areas, including aerodynamics, the integration of sensing (e.g., laser radars, vision-based systems, and millimeter-wave radars) and control, cooperative control with poorly modeled uncertainty, cascading failures in military and civilian applications, multi-mode seekers/sensor fusion, and data association problems and tracking systems. The contributions to this book explore these different strategies. The expression "optimization-directed" in this book's title is meant to suggest that the focus is not agonizing over whether optimization strategies identify a true global optimum, but rather whether these strategies make significant design improvements.
| ISBN: | 9780387282633 |
| Publication date: | 2nd December 2005 |
| Author: | Andrew Kurdila, P M Pardalos, Michael Zabarankin |
| Publisher: | Springer an imprint of Springer US |
| Format: | Hardback |
| Pagination: | 275 pages |
| Series: | Nonconvex Optimization and Its Applications |
| Genres: |
Optimization Cybernetics and systems theory Applied mathematics |
Robust design-that is, managing design uncertainties such as model uncertainty or parametric uncertainty-is the often unpleasant issue crucial in much multidisciplinary optimal design work. Recently, there has been enormous practical interest in strategies for applying optimization tools to the development of robust solutions and designs in several areas, including aerodynamics, the integration of sensing (e.g., laser radars, vision-based systems, and millimeter-wave radars) and control, cooperative control with poorly modeled uncertainty, cascading failures in military and civilian applications, multi-mode seekers/sensor fusion, and data association problems and tracking systems.
Robust Optimization-Directed Design features in the following genres: Optimization, Cybernetics and systems theory, Applied mathematics
Hardback. Not Available.
Robust Optimization-Directed Design was written by Andrew Kurdila, P M Pardalos, Michael Zabarankin and published by Springer an imprint of Springer US
Robust Optimization-Directed Design has 275 pages
Yes it is part of Nonconvex Optimization and Its Applications series