Enhanced Online Strip Crown Prediction Model Based on KCGAN-ELM for Imbalanced Dataset

다국어 초록 (Multilingual Abstract)

The strip crown directly affects the quality of strip steel. To enhance online crown control and product quality, an accurate and efficient strip crown prediction model is crucial. The accuracy of the prediction model is determined by the algorithm and dataset used in the modeling process. The accuracy can be enhanced by increasing algorithm complexity but it does not meet the requirements of online applications. Besides, the datasets collected for strip crown prediction are usually imbalanced, which impacts the modeling accuracy. In this paper, an enhanced strip crown prediction model based on KCGAN-ELM is established. A new hybrid algorithm KCGAN is proposed to deal with the imbalanced datasets. ELM is used to establish the strip crown prediction model. To meet the requirements of online applications, incremental learning is introduced, enabling the prediction model to update in real-time based on new production data. On this basis, an update strategy is devised to ensure the prediction model can maintain qualified prediction ability during the updating process. The experiment results indicate that the model trained with the dataset processed by KCGAN demonstrates a significantly enhanced prediction accuracy, achieving an RMSE of 2.86 μm and an R2 value of 0.97. The proposed update strategy enhances the stability of prediction capability during the model updating process.