2022
DOI: 10.1007/s00170-022-08825-w
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Application of machine learning to predict and diagnose for hot-rolled strip crown

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Cited by 30 publications
(12 citation statements)
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References 26 publications
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“…The role of generator is to construct a mapping function from prior noise distribution p z (z) to data space in order to learn the distribution of real sample data x and generate fake samples to deceive the discriminator as much as possible. The loss functions of the generator and discriminator are respectively shown in Equations ( 1) and (2).…”
Section: Cganmentioning
confidence: 99%
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“…The role of generator is to construct a mapping function from prior noise distribution p z (z) to data space in order to learn the distribution of real sample data x and generate fake samples to deceive the discriminator as much as possible. The loss functions of the generator and discriminator are respectively shown in Equations ( 1) and (2).…”
Section: Cganmentioning
confidence: 99%
“…In a nutshell, the generator is to generate data close to the distribution of real data, while the discriminator is twofold, (1) to judge whether the simulation process parameters G(z|y) match the conditional data y. (2) To evaluate the similarity between the generated data G(z|y) and the real data x.…”
Section: Cganmentioning
confidence: 99%
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“…By comparison with ANNs and regression trees, a hot-rolled strip crown prediction method based on SVM was developed. 11 Li et al 12 adopted an integrated computational fluid dynamics and SVM model to predict the cohesive zone in a blast furnace. Additionally, researchers have also tried to use various existing or improved optimisation algorithms applied in various research areas to optimise and improve the performance of learning models.…”
Section: Introductionmentioning
confidence: 99%
“…Ji et al [ 11 ] employed a genetic algorithm to optimize a multi-output support vector regression model for establishing a predictive model of the cross-sectional profile of strip steel. Song et al [ 12 ] employed the particle swarm optimization algorithm to optimize a support vector machine for constructing a predictive model of a hot rolling strip profile. Through comparison with artificial neural networks and regression trees, they validated the feasibility of this approach.…”
Section: Introductionmentioning
confidence: 99%