摘要
针对现有稀土元素组分含量模型具有离线、时滞大、抗干扰能力弱等问题,提出一种改进的GRA-即时学习算法(GRA-JITL-LSSVM)建立稀土萃取过程组分含量在线检测模型.首先,采用灰色关联分析方法(GRA)分析输入输出变量之间的变化趋势和关联程度,采用哈希表确定学习集大小,确保数据相似度信息的完整性和学习集的合理性,据此建立最小二乘支持向量机(LSSVM)模型,并引入数据库更新准则,提高模型的抗干扰能力;然后,为了保证GRA-JITL-LSSVM模型参数的全局最优,提出一种带有停滞回溯策略的遗传算法(SBS-GA),并对SBS-GA的收敛性进行分析验证;最后,通过镨/钕萃取现场数据进行仿真实验,结果表明所提出SBS-GA算法能够保证寻优参数的全局解,所提出的GRA-JITL-LSSVM实时性高、预测精度好,可用于稀土萃取生产现场元素组分含量的在线检测.
Aiming at the problems of off-line,large time delay,and weak anti-interference ability of existing rare earth element component content models,an improved GRA-just-in-time learning algorithm(GRA-JITL-LSSVM)is proposed to establish an online component content detection model for the rare earth extraction process.Firstly,the grey relational analysis(GRA)method is used to analyze the changing trend and correlation degree between input and output variables,and the hash table is used to determine the size of the learning set to ensure the integrity of the data similarity information and the rationality of the learning set.Based on this,the least squares support vector machine(LSSVM)model is established,and the database update criterion is introduced to improve the anti-interference ability of the model.Secondly,in order to ensure the global optimization of GRA-JITL-LSSVM model parameters,a genetic algorithm with a stagnation backtracking strategy(SBS-GA)is proposed,and the convergence of the SBS-GA is analyzed and verified.Finally,the simulation experiment is carried out based on Pr/Nd extraction field data.The results show that the proposed SBS-GA can ensure the global solution of the optimization parameters.The proposed GRA-JITL-LSSVM has high real-time performance and good prediction accuracy,which can be used for the online detection of the element component content in the production site of the rare earth extraction.
作者
陆荣秀
邓彪
杨辉
朱建勇
杨刚
代文豪
LU Rong-xiu;DENG Biao;YANG Hui;ZHU Jian-yong;YANG Gang;DAI Wen-hao(School of Electrical and Automation,East China Jiaotong University,Nanchang 330013,China;Key Laboratory of Advanced Control and Optimization of Jiangxi Province,Nanchang 330013,China)
出处
《控制与决策》
EI
CSCD
北大核心
2024年第2期458-466,共9页
Control and Decision
基金
国家重点研发计划项目(2020YFB1713700)
国家自然科学基金项目(61863014,61733005,61963015)
东北大学流程工业国家重点实验室开放基金项目(2021-KF-21-01)
江西省教育厅科技项目(GJJ200668)。
关键词
灰色关联分析法
即时学习
哈希表
组分含量
预测
停滞回溯策略
遗传算法
grey correlation analysis
just-in-time learning
Hash table
component content
prediction
stagnation backtracking strategy
GA
