基于稀疏化神经网络的浮选泡沫图像特征选择
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作者单位:

1.华东交通大学;2.西北工业大学

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TP181

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Selection Method for Froth Image Characters Based on Sparse Neural Network
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Affiliation:

1.East China Jiaotong University;2.Northwestern Polytechnical University

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    摘要:

    针对泡沫特征复杂繁多不利于建模控制的问题, 本文提出了一种基于稀疏化神经网络的泡沫图像特征选择方法. 相较于大部分稀疏模型以线性回归模型作为损失函数的情况, 本文选择以更为贴近实际工业过程非线性特点的神经网络模型作为损失函数, 并加入 范数约束以起到特征选择的效果; 此方法根据泡沫特征建立解决矿物品位回归问题的特征选择方法, 并采用近点梯度法计算最优解, 通过对第一层权值的综合排序得到特征选择子集; 最后, 利用支持向量机测试输入样本不同特征组合效果, 对比各特征子集得到浮选过程最优特征组合. 工业数据仿真结果表明, 该方法可以有效地实现泡沫图像维数约简.

    Abstract:

    Aiming at the problem that the froth characteristics are complicated and not conducive to the modeling control, this paper proposes a bubble image feature selection method based on sparse neural network. Compared with most sparse learning methods, the linear regression model is used as the loss function. The neural network model closer to the nonlinear actual industrial process is used as the loss function, and the 2,1-norm constraint condition is added to achieve the effect of feature selection. This method establishes a feature selection method based on the characteristics of the foam to solve the regression problem with the mineral level, and the optimal solution is calculated by the near-point gradient method. The comprehensive ranking of the first layer weights obtains the corresponding feature selection results. Finally, the support vector machine is used to detect the different feature combinations of the input samples, and the optimal feature combination of the flotation process is obtained. The industrial data simulation results show that the method can effectively realize the dimensional reduction of the bubble image.

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历史
  • 收稿日期:2019-12-22
  • 最后修改日期:2021-02-28
  • 录用日期:2020-03-06
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