控制理论与应用2026，Vol.43Issue(8)：1735-1747,13.DOI:10.7641/CTA.2025.50002
集成混合随机森林与KPCA的磨矿粒度随机增量建模
Hybrid random forest and KPCA based random incremental modeling for grinding particle size
摘要
Abstract
Grinding particle size is a key indicator for measuring the production quality of the grinding process,which directly affects the stability of the separation process,concentrate grade and metal recovery rate.However,due to the high cost,long detection time,and easy blockage of online particle size analyzers,it is difficult to perform accurate measurement of grinding particle size.Meanwhile,the broad learning system-based modeling methods face two main limitations:in-sufficient feature extraction capability and random allocation of node parameters in the enhancement layer.By integrating hybrid random forests(HRFs)with kernel principal component analysis(KPCA),a novel improved random incremental learning(RIL)soft measurement method called HRF-KPCA-RIL is proposed in this paper.Firstly,in the feature mapping layer,a hybrid forest group based on random forests and completely random forests is employed to replace the traditional neuron group for carrying out high-dimensional feature vector mapping.Secondly,the KPCA is utilized for feature extrac-tion from the high-dimensional mapping matrix,and the mutual information method is further applied to filter potential features and eliminate redundant information.Then,in the enhancement layer,high-quality nodes for soft measurement modeling are generated using the Greville iterative method-based global constraints to alleviate randomness issues.Final-ly,experimental studies based on real-valued function,benchmark datasets,and grinding particle size dataset validate the superiority and effectiveness of the proposed HRF-KPCA-RIL model.关键词
混合随机森林/核主成分分析/磨矿粒度/随机增量学习/Greville迭代Key words
hybrid random forests/kernel principal component analysis/grinding particle size/random incremental learning/Greville iteration引用本文复制引用
王前进,孙宇,代伟,马小平..集成混合随机森林与KPCA的磨矿粒度随机增量建模[J].控制理论与应用,2026,43(8):1735-1747,13.基金项目
国家自然科学基金项目(62003293,62373361),江苏省杰出青年基金项目(BK20240102),江苏省青蓝工程项目([2023]4)资助.Supported by the National Natural Science Foundation of China(62003293,62373361),the Science Fund for Distinguished Young Scholars of Jiangsu Province(BK20240102)and the Qinglan Project of Jiangsu Province([2023]4). （62003293,62373361）
