scholarly journals Robust model fitting via convex optimization techniques

2016 ◽  
Author(s):  
Yongfang Cheng
Keyword(s):  
Convex Optimization ◽  
Model Fitting ◽  
Optimization Techniques ◽  
Robust Model

Balancing of Flexible Rotors Using Convex Optimization Techniques: Optimum Min-Max LMI Influence Coefficient Balancing

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Costin D. Untaroiu ◽  
Paul E. Allaire ◽  
William C. Foiles
Keyword(s):  
Convex Optimization ◽  
Optimization Theory ◽  
Numerical Algorithms ◽  
Optimization Methods ◽  
Industrial Applications ◽  
Optimization Techniques ◽  
Linear Matrix ◽  
Influence Coefficient ◽  
Flexible Rotors ◽  
Optimum Solution

In some industrial applications, influence coefficient balancing methods fail to find the optimum vibration reduction due to the limitations of the least-squares optimization methods. Previous min-max balancing methods have not included practical constraints often encountered in industrial balancing. In this paper, the influence coefficient balancing equations, with suitable constraints on the level of the residual vibrations and the magnitude of correction weights, are cast in linear matrix inequality (LMI) forms and solved with the numerical algorithms developed in convex optimization theory. The effectiveness and flexibility of the proposed method have been illustrated by solving two numerical balancing examples with complicated requirements. It is believed that the new methods developed in this work will help in reducing the time and cost of the original equipment manufacturer or field balancing procedures by finding an optimum solution of difficult balancing problems. The resulting method is called the optimum min-max LMI balancing method.

Robust Model Fitting Using Higher Than Minimal Subset Sampling

2016 ◽  
Vol 38 (2) ◽  
pp. 350-362 ◽  
Author(s):  
Ruwan B. Tennakoon ◽  
Alireza Bab-Hadiashar ◽  
Zhenwei Cao ◽  
Reza Hoseinnezhad ◽  
David Suter
Keyword(s):  
Model Fitting ◽  
Robust Model ◽  
Minimal Subset
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