Results 231 to 240 of about 1,817,965 (264)

Numerical Methods for Robust Control

2008
A brief survey on the numerical properties of the methods for ${\cal H}_\infty$ design and μ-analysis and synthesis of linear control systems is given. A new approach to the sensitivity analysis of LMI --- based ${\cal H}_\infty$ design is presented that allows to obtain linear perturbation bounds on the computed controller matrices.
Petko Hr. Petkov, A. S. Yonchev, Nikolai D. Christov, Michail M. Konstantinov   +3 more
openaire   +1 more source

A Robust Method for Multivariate Regression

2000
We introduce a new method for multivariate regression based on robust estimation of the location and scatter matrix of the joint response and explanatory variables. The resulting method has good equivariance properties and the same breakdown value as the initial estimator for location and scatter.
Van Aelst, S., van Driessen, K., Rousseeuw, P.J.   +2 more
openaire   +2 more sources

Robust Multivariable Tuning Methods

2012
The rapid growth in the complexity of modern process plants both in terms of material flow and energy exchange have substantially increased the number of feedback control loops for maintaining desired production conditions and product quality. Traditionally, PID controllers are used in large numbers in all industries as a single-loop controller.
openaire   +2 more sources

A Method for Robust Index Tracking

2011
In today’s Portfolio Management many strategies are based on the investment into indices. This is a consequence of various empirical studies that show that the allocation over asset classes, countries etc. provides a greater performance contribution than the selection of single assets. For every portfolio containing indices as components the problem is
Denis Karlow, Peter Roßbach
openaire   +1 more source

Robust Methods for Data Reduction

2016
Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double ...
FARCOMENI, Alessio, Luca Greco
openaire   +3 more sources

Robust Methods

2021
J.S. Marron, Ian L. Dryden
openaire   +1 more source

Tent Method, optimization and robustness

Optimal Control Applications and Methods, 2004
AbstractThe Tent Method solves different extremal problems (minima and maxima of smooth functions, minimax problems, optimization problems, etc.). A detailed description of the Method with its geometrical and topological foundations is given. The Tent Method has application to optimization problems; in particular, it allows to prove the Maximum ...
openaire   +2 more sources

Soft Methods in Robust Statistics

2010
The focus is on robust regression methods for problems where the predictor matrix has full rank and where it is rank deficient. For the first situation, various robust regression methods have been introduced, and here an overview of the most important proposals is given. For the latter case, robust partial least squares regression is discussed. The way
openaire   +1 more source

Robust Methods for Distributed Learning

This thesis develops robust and efficient aggregation methods for distributed learning and explores their vulnerabilities. Distributed learning paradigms, such as federated and decentralized learning, enable the coordination of models across a collection of agents without the need to exchange raw data.
openaire   +2 more sources

Robust Methods

2014
Peter Meer, Sushil Mittal
openaire   +1 more source