南京信息工程大学学报2026，Vol.18Issue(2)：247-254,8.DOI:10.13878/j.cnki.jnuist.20250313001
Nox浓度的混合正则化块增量随机配置网络预测方法
Mixed regularization block incremental stochastic configuration network for NOx concentration prediction
摘要
Abstract
To achieve rapid and accurate prediction of NOx emissions in Municipal Solid Waste Incineration(MSWI)processes,this paper proposes a method based on a Mixed Regularization Block incremental Stochastic Configuration Network(MR-BSCN).After completing the model training with BSCN,redundant nodes are pruned by incorporating a momentum term into the convex optimization-approximated L0 regularization.Meanwhile,to ensure the accuracy of the pruned model,L2 regularization is applied to fine-tune the output weights.Finally,verification using real-world data from an MSWI plant in Beijing demonstrates that the proposed method achieves accurate NOx concentration prediction with a more compact model structure,building upon the BSCN's fast modeling capability.This work lays a foundation for the optimal control of NOx emissions.关键词
城市固废焚烧/Nox预测/块增量随机配置网络/正则化Key words
municipal solid waste incineration(MSWI)/NOx concentration prediction/block incremental stochas-tic configuration network(BSCN)/regularization分类
资源环境引用本文复制引用
严爱军,卜宝..Nox浓度的混合正则化块增量随机配置网络预测方法[J].南京信息工程大学学报,2026,18(2):247-254,8.基金项目
国家自然科学基金(62373017) （62373017）
