Multivariate uncertain risk aversion with application to accounts receivables pricing

Wang, Ke; Huang, Xiaolin; Hu, Shuang; Wang, Hongwei; Zhao, Mingxuan; Zhou, Jian

doi:10.1007/s00500-022-07272-9

Cited by 1 publication

(1 citation statement)

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“…Moreover, this material assumes a critical role in assuring the quality of subsequent steel production processes. [ 1 ] For example, the hot‐rolled strip crown will directly affect the manufacturing of cold‐rolled products. Nevertheless, this attribute will be susceptible to influence from various parameters, [ 2 ] resulting in a notably intricate control process.…”

Section: Introductionmentioning

confidence: 99%

Industrial Big Data‐Driven Modeling and Prediction for Hot‐Rolled Strip Crown with Multigrade and Multispecification Data

Xu,

Ding,

Liu

et al. 2024

steel research int.

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In the field of hot rolling big data, the presence of different steel types, specifications, and data heterogeneity poses significant challenges to the accuracy and stability of using single machine learning regression technology for prediction. Therefore, this study proposes a hot‐rolled strip crown prediction method that combines data clustering and fusion modeling. First, this article introduces a relevant mechanism for designing cluster strategies. The optimal clustering strategy is determined through comparative experiments using rolling process parameters, strip size, and main material components as the clustering features. Subsequently, the K‐Means++ algorithm is used to effectively cluster the training and testing datasets based on this strategy, generating multiple clusters for both datasets. Finally, this study establishes seven different training models to match the most suitable regression prediction model for each cluster, and matching between each cluster and the model is determined through rigorous testing. The evaluation of the fusion model shows an R2 value of 0.829 and a root mean square error value of 3.974. The experimental results show that the proposed method outperforms traditional methods in solving the challenges of multiclass classification and data heterogeneity, providing strong data support for the intelligent control of the hot‐rolled strip crown in the future.

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Section: Introductionmentioning

confidence: 99%