2020
DOI: 10.1109/access.2020.3003741
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Analyzing the Nearly Optimal Solutions in a Multi-Objective Optimization Approach for the Multivariable Nonlinear Identification of a PEM Fuel Cell Cooling System

Abstract: In this work, the parametric identification of a cooling system in a PEM (proton exchange membrane) fuel cell is carried out. This system is multivariable and nonlinear. In this type of system there are different objectives and the unmodeled dynamics cause conflicting objectives (prediction errors in each output). For this reason, resolution is proposed using a multi-objective optimization approach. Nearly optimal alternatives can exist in any optimization problem. Among them, the nearly optimal solutions that… Show more

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Cited by 4 publications
(1 citation statement)
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References 28 publications
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“…Therefore, it is necessary to use existing technologies to improve computing power. With the in-depth learning training, the convolution kernel will extract more useful information from the image or feature map through autonomous adjustment, which is called feature learning [11][12].…”
Section: Deep Network Improvementmentioning
confidence: 99%
“…Therefore, it is necessary to use existing technologies to improve computing power. With the in-depth learning training, the convolution kernel will extract more useful information from the image or feature map through autonomous adjustment, which is called feature learning [11][12].…”
Section: Deep Network Improvementmentioning
confidence: 99%