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提出了基于支持向量回归的钢材力学性能建模方法,采用遗传算法优化支持向量回归模型的参数,避免了参数选择的盲目性,使得支持向量回归模型的预测性能有了显著提高。将此方法应用于实际钢厂的钢材力学性能预报中,模型的训练与验证数据都来自于实际的过程,结果表明采用遗传优化的支持向量回归模型对钢材力学性能具有很好的预估性能。
The method of steel mechanical properties modeling based on support vector regression is proposed. The genetic algorithm is used to optimize the parameters of support vector regression model, which avoids the blindness of parameter selection and improves the prediction performance of support vector regression model significantly. Applying this method to the prediction of the mechanical properties of steel in the actual steel mills, the training and verification data of the model come from the actual process. The results show that the genetic optimization support vector regression model has a good predictive performance on the mechanical properties of the steel.