基于WGKSOM-DRCA自适应即时学习的转炉炼钢终点碳温软测量方法
TP391; 转炉炼钢终点碳温的准确预测是实现转炉终点控制的关键.针对转炉生产过程数据存在波动性大和非线性特点引起传统即时学习度量的算法学习集质量低,进而削弱模型预测性能的问题,提出了一种基于加权高斯核自组织映射动态相关成分分析(WGKSOM-DRCA)自适应即时学习软测量建模方法用于转炉炼钢终点碳温预测.首先,采用引入标签信息的WGK度量准则构造WGKSOM聚类算法引导聚类方向,提高算法的聚类质量并降低类簇数据波动性对于建模的影响;其次,利用高斯后验概率计算待测样本的隶属度并通过引入动态因子构建DRCA度量策略,从而实现自适应的样本选择,进一步提升待测样本对应的局部算法学习集质量并用于局部模...
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| Published in | 计算机集成制造系统 Vol. 30; no. 1; pp. 103 - 117 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
昆明理工大学信息工程与自动化学院,云南 昆明 650500
31.01.2024
昆明理工大学云南省人工智能重点实验室,云南 昆明 650500%云南昆钢电子信息科技有限公司,云南 昆明 650302 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1006-5911 |
| DOI | 10.13196/j.cims.2022.0561 |
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| Abstract | TP391; 转炉炼钢终点碳温的准确预测是实现转炉终点控制的关键.针对转炉生产过程数据存在波动性大和非线性特点引起传统即时学习度量的算法学习集质量低,进而削弱模型预测性能的问题,提出了一种基于加权高斯核自组织映射动态相关成分分析(WGKSOM-DRCA)自适应即时学习软测量建模方法用于转炉炼钢终点碳温预测.首先,采用引入标签信息的WGK度量准则构造WGKSOM聚类算法引导聚类方向,提高算法的聚类质量并降低类簇数据波动性对于建模的影响;其次,利用高斯后验概率计算待测样本的隶属度并通过引入动态因子构建DRCA度量策略,从而实现自适应的样本选择,进一步提升待测样本对应的局部算法学习集质量并用于局部模型训练,最终输出终点碳温的预测结果.实验表明,所提算法在转炉炼钢终点碳温预测上相对于其他算法有更好的表现,在±0.02％的预测误差范围,碳含量的预测精度为92％,在±10℃的误差范围,温度的预测精度为93.5％. |
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| AbstractList | TP391; 转炉炼钢终点碳温的准确预测是实现转炉终点控制的关键.针对转炉生产过程数据存在波动性大和非线性特点引起传统即时学习度量的算法学习集质量低,进而削弱模型预测性能的问题,提出了一种基于加权高斯核自组织映射动态相关成分分析(WGKSOM-DRCA)自适应即时学习软测量建模方法用于转炉炼钢终点碳温预测.首先,采用引入标签信息的WGK度量准则构造WGKSOM聚类算法引导聚类方向,提高算法的聚类质量并降低类簇数据波动性对于建模的影响;其次,利用高斯后验概率计算待测样本的隶属度并通过引入动态因子构建DRCA度量策略,从而实现自适应的样本选择,进一步提升待测样本对应的局部算法学习集质量并用于局部模型训练,最终输出终点碳温的预测结果.实验表明,所提算法在转炉炼钢终点碳温预测上相对于其他算法有更好的表现,在±0.02％的预测误差范围,碳含量的预测精度为92％,在±10℃的误差范围,温度的预测精度为93.5％. |
| Abstract_FL | Accurate endpoint prediction of Basic Oxygen Furnace(BOF)carbon content and temperature is the key to realize endpoint control.To solve the problem of low quality of similar samples by traditional Just-In-Time Learning(JITL)measurement due to the high volatility and nonlinear characteristics of BOF process data,an adaptive JITL soft-sensing method based on Weighted Gaussian Kernel Self-Organization Map Dynamic Relevant Component Anal-ysis(WGKSOM-DRCA)was proposed for endpoint prediction of BOF carbon content and temperature.The WGK-SOM clustering algorithm was proposed by using the WGK metric criterion introducing label information to guide the clustering direction and improve the clustering quality of algorithm and reduce the influence of data volatility.The Gaussian posterior probability was used to calculate the membership degree of the test samples and the appro-priate learning set was selected adaptively to predict the endpoint carbon content and temperature by introducing dy-namic factors and using DRCA metric learning strategy.Results showed that the proposed algorithm performed bet-ter than other algorithms in predicting endpoint carbon content and temperature of BOF steelmaking.The prediction accuracy of carbon content was 92％within the error range of±0.02％,and the prediction accuracy of temperature was 93.5％within the error range of±10℃. |
| Author | 刘辉 陈甫刚 刘建勋 陈棕鑫 |
| AuthorAffiliation | 昆明理工大学信息工程与自动化学院,云南 昆明 650500;昆明理工大学云南省人工智能重点实验室,云南 昆明 650500%云南昆钢电子信息科技有限公司,云南 昆明 650302 |
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| Author_FL | CHEN Zongxin LIU Jianxun LIU Hui CHEN Fugang |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitle_FL | Soft-sensing method for endpoint prediction of BOF carbon content and temperature based on WGKSOM-DRCA adaptive JITL |
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| Keywords | 自组织映射 just-in-time-learning self organization map 即时学习 转炉炼钢 高斯核函数 soft sensor relevant component analysis 相关成分分析 软测量 basic oxygen furnace Gaussian kernel function |
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| PublicationTitle | 计算机集成制造系统 |
| PublicationTitle_FL | Computer Integrated Manufacturing Systems |
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| Title | 基于WGKSOM-DRCA自适应即时学习的转炉炼钢终点碳温软测量方法 |
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