个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:控制科学与工程学院
学科:控制理论与控制工程
办公地点:创新园大厦A614
联系方式:刘全利 大连理工大学控制科学与工程学院 邮编:116024 电话:0411-84705516
电子邮箱:liuql@dlut.edu.cn
Data-driven modeling by Gaussian membership based sample selection and its application in steel energy system
点击次数:
论文类型:会议论文
发表时间:2014-06-29
收录刊物:EI、Scopus
卷号:2015-March
期号:March
页面范围:377-384
摘要:Due to the data diversity and complexity in industrial system, the accuracy of data-based modeling might be largely affected by such a series of issues. Aiming at the energy system in steel industry, this study proposes a fuzzy modeling based on Gaussian membership expression. First, in the stage of sample selection, the industrial data set is divided into a number of clusters, from which the representative sample are chosen based on a variable step rule. Second, given the industrial data usually accompany with high level noise and anomaly points, a fuzzy modeling based on Gaussian membership is proposed, where a sample reliability coefficient is introduced to alleviate the negative impact produced by ill-posed data, and the model parameters solution is explicitly derived later. The proposed method has been applied to the practice of gas flow prediction in a steel plant. To verify its performance, a number of experiments are conducted by using the data coming from the energy center in the plant. The results indicate that the proposed method greatly improves the prediction accuracy and efficiency, which plays a significant role in data-based modeling for the industrial system. ? 2014 IEEE.