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The Research of the Flotation Recovery Prediction Methods Based on Advanced LS-SVM

机译:基于高级LS-SVM的浮选回收率预测方法研究

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Flotation recovery is an important index of flotation process, in order to change the existing detection methods of low accuracy,a soft measurement model of flotation recoveries is proposed based on improved weighted LS -SVM. According to the flotation foam characteristics and the corresponding relation of flotation recovery, the fuzzy C-means clustering method is used for flotation characteristics of data processing, the image characteristic values as prediction model input and using genetic algorithm to optimize the parameters of the model. The result show that the modified algorithm can overcome a prediction standard model LS -SVM algorithm parameter optimization shortage, and have better forecasting effect which provide effective protection for flotation process operation and flotation operation stable operation optimization .
机译:浮选回收率是浮选工艺的重要指标,为了改变现有的低精度检测方法,提出了一种基于改进的加权最小二乘支持向量机的浮选回收率软测量模型。根据浮选泡沫的特性和浮选回收率的对应关系,采用模糊C-均值聚类方法对浮选数据进行处理,将图像特征值作为预测模型的输入,并采用遗传算法对模型的参数进行优化。结果表明,改进后的算法可以克服预测标准模型LS-SVM算法参数优化的不足,具有较好的预测效果,为浮选工艺运行和浮选稳定运行优化提供了有效的保护。

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