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For the purpose, we develop a multi-objective optimization model for robust microgrid planning, on the basis of an economic robustness measure, i.e. the worst-case cost among possible scenarios, to reduce the variability among scenario costs caused by uncertainties.
Golpîra et al. (2017) developed a bi-level programming model for robust design of a green supply chain network.
In this paper, by using pairwise metric learning, we present a novel appearance model for robust visual tracking.
A statistical model calibration method, used to improve the accuracy of the simulation model for robust optimization, was proposed and implemented, based on the uncertainty data.
We present in this paper a new model for robust combinatorial optimization with cost uncertainty that generalizes the classical budgeted uncertainty set.
We consider a model for robust network design in telecommunications, in which we minimize the cost of the maximum mismatch between supply and demand.
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Zhang et al. [43] use matrix norm-based regression models for robust face recognition.
This paper presents models for analyzing the propagation of dimensional variation in multi-stage compliant assembly systems and the use of such models for robust design and adaptive control of assembly quality.
(2) Model validation for robust control.
The author previously proposed a neural network model neocognitron for robust visual pattern recognition.
The aim of this paper is to remedy the ill-posedness in model validation for robust control.
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