首页|基于稀疏表示剪枝集成建模的烧结终点位置智能预测

基于稀疏表示剪枝集成建模的烧结终点位置智能预测

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
烧结终点位置(BTP)是烧结过程至关重要的参数，直接决定着最终烧结矿的质量。由于BTP难以直接在线检测，因此，通过智能学习建模来实现BTP的在线预测并在此基础上进行操作参数调节对提高烧结矿质量具有重要意义。针对这一实际工程问题，首先提出一种基于遗传优化的Wrapper特征选择方法，可选取使后续预测建模性能最优的特征组合;在此基础上，为了解决单一学习器容易过拟合的问题，提出了基于随机权神经网络(RVFLNs)的稀疏表示剪枝(SRP)集成建模算法，即SRP-ERVFLNs算法。所提算法采用建模速度快、泛化性能好的RVFLNs作为个体基学习器，采用对基学习器基函数与隐层节点数等参数进行扰动的方式来增加集成学习子模型间的差异性;同时，为了进一步提高集成模型的泛化性能与计算效率，引入稀疏表示剪枝算法，实现对集成模型的高效剪枝;最后，将所提算法用于烧结过程BTP的预测建模。工业数据实验表明，所提方法相比于其他方法具有更好的预测精度、泛化性能和计算效率。
Intelligent prediction of burning through point based on sparse representation pruning ensemble modeling
The burning through point(BTP)is a crucial parameter in sintering process,which directly determines the quality of the final sinter.Since the BTP is difficult to directly detect online,it is of great significance to realize the online prediction of BTP through intelligent learning modeling and adjust the operating parameters on this basis to improve the quality of sinter.Aiming at this practical engineering problem,a Wrapper feature selection method based on the genetic algorithm is firstly proposed in this paper,which can select the feature combination that optimizes the subsequent predictive modeling performance as much as possible.Secondly,in order to solve the problem of easy overfitting in intelligent modeling of a single learner,a sparse representation pruning(SRP)ensemble modeling algorithm based on the random vector functional-link networks(RVFLNs)is proposed,namely SRP-ERVFLNs.The proposed method uses RVFLNs with fast modeling speed and good generalization performance as individual base learners,and perturbs the parameters of the base learner to increase the difference between the ensemble learning sub-models.At the same time,in order to further improve the generalization performance and computational efficiency of the ensemble model,a sparse representation pruning algorithm is introduced to achieve effective pruning of the ensemble model.Finally,the proposed SRP-ERVFLNs algorithm is used for prediction modeling of the BTP in the sintering process.Experiments using industrial data show that the proposed method has better prediction accuracy,generalization performance and computational efficiency than other methods.

intelligent predictionfeature selectionensemble learningsparse representationpruningburning through point(BTP)random vector functional-link networks(RVFLNs)

周平、吴忠卫、张瑞垚、吴永建

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东北大学流程工业综合自动化国家重点实验室,辽宁沈阳 110819

中国矿业大学煤炭加工与高效洁净利用教育部重点实验室,江苏徐州 221116

智能预测 特征选择 集成学习 稀疏表示 剪枝 烧结终点位置 随机权神经网络(RVFLNs)

国家自然科学基金国家自然科学基金兴辽英才项目

U22A204961890934XLYC1907132

2024

10.7641/CTA.2022.20190

控制理论与应用
华南理工大学 中国科学院数学与系统科学研究院

控制理论与应用

CSTPCD北大核心
影响因子：1.076
ISSN：1000-8152
年,卷(期)：2024.41(3)
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