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Rolling force prediction based on multiple support vector machines

机译:基于多个支持向量机的轧制力预测

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摘要

Accurate rolling force setting is very important for hot strip rolling, but it is difficult to obtain accurate mathematical models for it. A rolling force prediction method based on multiple support machines is proposed in this paper. In order to classify the sample data, the input space of the model is divided into several subspaces utilizing the subtractive clustering method firstly, and several sub support vector machine models are established according to the number of the subspace. The sub models are trained using the actual sampled data, then the output of the sub models are synthesized utilizing the principle component analysis method. Experiment results show that the proposed method can achieve promising performance. The prediction average error rate decreases from 8.19% by BP-NN to 3.76% by the proposed method.
机译:精确的轧制力设置对于热带轧制非常重要,但很难获得精确的数学模型。本文提出了一种基于多支撑机器的轧制力预测方法。为了对样本数据进行分类,模型的输入空间首先分为利用减簇聚类方法的多个子空间,并且根据子空间的数量建立了几个子支持向量机模型。子模型使用实际采样数据训练,然后使用原理分析方法合成子模型的输出。实验结果表明,该方法可以实现有前途的性能。通过所提出的方法,预测平均误差率从BP-NN的8.19%降低至3.76%。

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