数据驱动的锑粗选泡沫图像特征优化设定-控制与决策.PDF

第31 卷 第7 期 控 制 与 决 策 2016 年 7 月 Vol. 31 No. 7 Control and Decision Jul. 2016 文章编号: 1001-0920 (2016) 07-1206-07 DOI: 10.13195/j.kzyjc.2015.0591 数据驱动的锑粗选泡沫图像特征优化设定 吴 佳, 谢永芳, 阳春华, 桂卫华 ( 中南大学信息科学与工程学院，长沙410083) 摘 要: 针对锑浮选过程中精、尾矿品位难以在线检测, 浮选性能不稳定的问题, 提出一种数据驱动的泡沫图像特征 优化设定方法. 该方法根据入矿品位类型对泡沫图像特征进行优化设定, 并针对不同入矿品位类型的样本分布特点, 先尝试采用案例推理的方法从历史数据中寻找浮选性能优良的泡沫状态. 若经验知识不足, 则采用基于多中心模糊 C 均值聚类与概率支持向量回归的区间II 型模糊系统建模方法建立精、尾矿品位指标模型, 并在此基础上利用智能 优化方法寻优泡沫图像特征值. 某锑浮选工业实验结果表明了所提出方法的有效性. 关键词: 数据驱动；锑浮选；案例推理；模糊C 均值聚类；支持向量回归 中图分类号: TP273 文献标志码: A Data-driven optimal setting for froth image features of stibium rougher flotation WU Jia, XIE Yong-fang , YANG Chun-hua, GUI Wei-hua (School of Information Science and Technology，Central South University，Changsha 410083，China ．Correspondent: XIE Yong-fang ，E-mail ：yfxie@.csu.edu.cn) Abstract: Due to the difficulties of measuring concentrate and tailing grade online and unstability of the flotation performance, a data-driven optimal setting for froth image features is proposed. The froth image features are optimal set according to the type of feed ore grade. Considering the distribution nature of samples of each feed grade type, it is tried to use case-based reasoning method for obtaining the froth status with optimal flotation performance from history data. When lack of enough experiential knowledge, the interval type II fuzzy system modeling method based on the multi-centers fuzzy C-means clustering and probabilistic support vector regress method is adopted to build the concentrate and tailing grade model. Then intelligent optimization algorithm is applied to search