[1]严爱军,郝晨.基于混合分布加权M估计和自适应正则化的随机配置网络[J].智能系统学报,2025,20(6):1392-1403.[doi:10.11992/tis.202501023]
YAN Aijun,HAO Chen.Stochastic configuration networks based on mixed distribution weighted M-estimation and adaptive regularization[J].CAAI Transactions on Intelligent Systems,2025,20(6):1392-1403.[doi:10.11992/tis.202501023]
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YAN Aijun,HAO Chen.Stochastic configuration networks based on mixed distribution weighted M-estimation and adaptive regularization[J].CAAI Transactions on Intelligent Systems,2025,20(6):1392-1403.[doi:10.11992/tis.202501023]
基于混合分布加权M估计和自适应正则化的随机配置网络
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第6期
页码:
1392-1403
栏目:
学术论文—机器学习
出版日期:
2025-11-05
- Title:
- Stochastic configuration networks based on mixed distribution weighted M-estimation and adaptive regularization
- Keywords:
- neural network; stochastic configuration networks; parameter prediction; M-estimation; mixture distribution; L2 regularization; model robustness; model generalization; municipal solid waste incineration
- 分类号:
- TP183
- 摘要:
- 为提升随机配置网络(stochastic configuration networks, SCNs)的鲁棒性和泛化性，提出了一种基于混合分布加权M估计和自适应正则化的SCN建模方法。采用高斯和柯西混合分布加权M估计获得训练样本的惩罚权重，根据训练数据对模型的贡献度评估其输出权重，以增强模型鲁棒性；根据建模残差变化情况和隐节点数分配合适的L2正则化参数，以保证模型具有较好的泛化性。通过4个标准数据集和城市固废焚烧过程的历史数据对该方法的性能进行实验测试。实验结果表明，基于本文所提方法构建的参数模型在鲁棒性和泛化性上相对于其他对比方法具有优势，从而拓宽了SCN的应用范围。
- Abstract:
- To enhance the robustness and generalization capability of stochastic configuration networks (SCNs), this paper proposes a novel SCN modeling method based on mixed-distribution weighted M-estimation and adaptive regularization. First, a Gaussian–Cauchy mixed-distribution weighted M-estimation is employed to determine the penalty weights of training samples, and the output weights of the model are evaluated according to the contribution of each training instance, thereby improving model robustness. Second, an adaptive L2 regularization parameter is assigned based on the variation of modeling residuals and the number of hidden nodes, ensuring that the model maintains good generalization. Finally, the effectiveness of the proposed method is empirically validated using four benchmark datasets and historical data from municipal solid waste incineration processes. Experimental results demonstrate that the parameter models developed using the proposed method outperform comparative methods in both robustness and generalization, thereby broadening the application scope of SCNs.
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备注/Memo
收稿日期:2025-1-15。
基金项目:国家自然科学基金项目(62373017，62073006)；北京市自然科学基金项目(4212032).
作者简介:严爱军，博士生导师，教授，主要研究方向为复杂过程建模与智能优化控制方法。发表学术论文100余篇。E-mail：yanaijun@bjut.edu.cn。;郝晨，硕士研究生，主要研究方向为复杂过程建模与智能优化控制方法。E-mail：haochen@emails.bjut.edu.cn。
通讯作者:严爱军. E-mail：yanaijun@bjut.edu.cn
更新日期/Last Update:
1900-01-01