Probabilistic and Randomized Methods for Design under Uncertainty

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Probabilistic and Randomized Methods for Design under Uncertainty

Calafiore, Giuseppe (EDT)/ Dabbene, Fabrizio (EDT)

ウェブストア価格 ¥69,228（本体¥62,935）
Springer Verlag（2006/02発売）
外貨定価 US$ 329.99
ポイント 629pt

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製本 Hardcover:ハードカバー版／ページ数 457 p.／サイズ 21 illus.
言語 ENG
商品コード 9781846280948
DDC分類 620.004205196

Full Description

In many engineering design and optimization problems, the presence of uncertainty in the data is a critical issue. There are different ways to describe this uncertainty and to devise designs that are partly insensitive or robust to it.

This book examines uncertain systems in control engineering and general decision or optimization problems for which data is uncertain. Written by leading researchers in optimization and robust control; it highlights the interactions between these two fields.

Part I describes theory and solution methods for probability-constrained and stochastic optimization problems;

Part II focuses on numerical methods for solving randomly perturbed convex programs and semi-infinite optimization problems by probabilistic techniques;

Part III details the theory and applications of randomized techniques to the analysis and design of robust control systems.

It will interest researchers, academics and postgraduates in control engineering and operations research as well as professionals working in operations research.

Chance-Constrained and Stochastic Optimization.- Scenario Approximations of Chance Constraints.- Optimization Models with Probabilistic Constraints.- Theoretical Framework for Comparing Several Stochastic Optimization Approaches.- Optimization of Risk Measures.- Robust Optimization and Random Sampling.- Sampled Convex Programs and Probabilistically Robust Design.- Tetris: A Study of Randomized Constraint Sampling.- Near Optimal Solutions to Least-Squares Problems with Stochastic Uncertainty.- The Randomized Ellipsoid Algorithm for Constrained Robust Least Squares Problems.- Randomized Algorithms for Semi-Infinite Programming Problems.- Probabilistic Methods in Identification and Control.- A Learning Theory Approach to System Identification and Stochastic Adaptive Control.- Probabilistic Design of a Robust Controller Using a Parameter-Dependent Lyapunov Function.- Probabilistic Robust Controller Design: Probable Near Minimax Value and Randomized Algorithms.- Sampling Random Transfer Functions.- Nonlinear Systems Stability via Random and Quasi-Random Methods.- Probabilistic Control of Nonlinear Uncertain Systems.- Fast Randomized Algorithms for Probabilistic Robustness Analysis.