Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accurac...Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples. In order to reduce the errors of manual classification, an automatic clustering algorithm is proposed and applied to diagnose down-hole conditions of pumping systems. The spectral clustering （SC） is a new clustering algorithm, which is suitable for any data distribution. However, it is sensitive to initial cluster centers and scale parameters, and needs to predefine the cluster number. In order to overcome these shortcom- ings, we propose an automatic clustering algorithm, fast black hole-spectral clustering （FBH-SC）. The FBH algo- rithm is used to replace the K-mean method in SC, and a CritC index function is used as the target function to automatically choose the best scale parameter and clus- tering number in the clustering process. Different simulation experiments were designed to define the relationship among scale parameter, clustering number, CritC index value, and clustering accuracy. Finally, an example is given to validate the effectiveness of the proposed algorithm.展开更多
基金the National Natural Science Foundation of China (Grant No. 61403040)
文摘Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples. In order to reduce the errors of manual classification, an automatic clustering algorithm is proposed and applied to diagnose down-hole conditions of pumping systems. The spectral clustering （SC） is a new clustering algorithm, which is suitable for any data distribution. However, it is sensitive to initial cluster centers and scale parameters, and needs to predefine the cluster number. In order to overcome these shortcom- ings, we propose an automatic clustering algorithm, fast black hole-spectral clustering （FBH-SC）. The FBH algo- rithm is used to replace the K-mean method in SC, and a CritC index function is used as the target function to automatically choose the best scale parameter and clus- tering number in the clustering process. Different simulation experiments were designed to define the relationship among scale parameter, clustering number, CritC index value, and clustering accuracy. Finally, an example is given to validate the effectiveness of the proposed algorithm.
文摘从高维的生物医学数据中探索发现与疾病相关的基因是目前的热点研究问题,但是大部分生物医学数据具有许多与寻找疾病基因不相关或冗余特征,很难直接投入使用.针对这个问题,提出了一种自适应双种群混合磷虾黑洞算法(modified binary krill herd and black hole algorithm, MBKHA).该算法将改进的二进制磷虾算法与二进制黑洞算法相结合,磷虾算法负责寻找更优的解集,黑洞算法负责加快算法收敛,通过使用自适应划分规则动态调控种群中磷虾个体和恒星个体的数量,从而实现两个算法优势互补.基于5个公开医学微阵列数据集,从多个指标比较了提出的方法和其他特征选择算法的性能,实验结果表明该方法在特征选择上具有更好的性能.
