基于模糊支持向量机的硫浮选工况识别

Performance recognition of sulfur flotation based on fuzzy support vector machine

针对硫浮选泡沫图像噪声大、特征重要度差异显著引起工况难以识别的问题,提出基于模糊支持向量机的硫浮选工况识别方法。通过融合样本模糊隶属度和特征信息增益,获取图像视觉特征的特征重要度;并结合特征重要度矩阵,改进模糊支持向量机的核函数,进而建立工况类别与图像特征之间的关系模型,实现硫浮选工况识别。采用模糊隶属度对噪声赋予较小的权值,并结合模糊隶属度来获取特征重要度矩阵,可以减小噪声样本的影响,以揭示图像特征重要度之间的差异,提高工况识别准确性。锌直接浸出冶炼硫浮选生产过程的实际测试数据验证了方法的有效性。

Considering performance recognition problem caused by the high noise of froth images and the obvious difference of feature importance in sulfur flotation process,a performance recognition method for sulfur flotation process using fuzzy support vector machine was proposed. With the combination of fuzzy membership and feature information gain,the image feature importance was obtained,and the kernel function of fuzzy support vector machine was improved using the feature importance. Then,the model that reveals the relationship between performance and image feature was established to detect sulfur condition. As the fuzzy membership was used to define a small weight for the noise sample and acquire feature importance,which can reduce the effect of image noise points and reveal the difference of feature importance,the classification accuracy is effectively improved. The simulation results show the effectiveness by using actual running data from a sulfur flotation process of zinc direct leaching hydrometallurgy.