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基于多模態和加權支持向量機的熱軋軋制力智能預報

Intelligent prediction of rolling force in hot rolling based on a multi-model and weighted support vector machine

  • 摘要: 為了提高熱軋生產過程精軋機組的軋制力預設定精度,需要對軋制力進行高精度的預報.本文通過機理公式計算出軋制力的近似值,然后采集大量的實際生產數據修正軋制力預報值.首先利用聚類方法區分不同的生產狀態,其次在相同生產狀態下采用加權最小二乘支持向量機計算軋制力的修正系數,最后采用乘法方式修正軋制力,達到高精度的軋制力預測.結果表明,軋制力預報的平均相對誤差為3.2%,滿足現場的生產要求.

     

    Abstract: In order to improve the set accuracy of rolling force for a finishing mill in the hot rolling process, high precision prediction of rolling force is very important. In this paper, an approximate value of rolling force is calculated through theoretical formulae. And then, a correction coefficient of rolling force is computed using big field data. Firstly, different product states are classfied by the clustering method. Secondly, the correction coefficient is computed based on a weighted least square support vector machine. Through a combination of these two results, the rolling force value with high precision is predicted. The average relative prediction error of rolling force is 3.2%, which can meet the requirements of field production.

     

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