控制理论与应用2025，Vol.42Issue(8)：1632-1640,9.DOI:10.7641/CTA.2024.30087
带有Dropout结构的贝叶斯近似宽度学习系统
Bayesian approximate broad learning system with dropout structure
摘要
Abstract
The existing broad learning system(BLS)and its improved algorithms have a common problem,that is,with the increasing complexity of data in practical scenarios,the network structure becomes extremely complex,resulting in the consumption of computing resources increased greatly.To handle the problem,this paper proposes a Bayesian approximate broad learning system with dropout structure(Dropout-BABLS).Firstly,the dropout algorithm is used to randomly discard the hidden layer nodes of broad learning system.Secondly,by combining the Gaussian regression process and Bayesian theory to approximate the loss function of Dropout on the output results,the objective function of Dropout-BABLS is determined.Next,the augmented Lagrange multiplier method is used to optimize the output weight of the objective function.Finally,the analysis and evaluation of the algorithm 10 sets of regression data of UCI machine learning knowledge base and 6 sets of time series data builted by ourselves.The results show that the developed algorithm by Dropout-BABLS can maintain the corresponding output accuracy and reduce the training time by 25％to 50％.关键词
宽度学习系统/Dropout/高斯过程/贝叶斯近似/拉格朗日乘子/回归分析Key words
broad learning system/dropout/Gaussian process/Bayesian approximate/Lagrange multipliers/regression analysis引用本文复制引用
陈滔,王立杰,刘洋,徐丽莉,于海生..带有Dropout结构的贝叶斯近似宽度学习系统[J].控制理论与应用,2025,42(8):1632-1640,9.基金项目
国家自然科学基金项目(62103214,62373208,62273189),中国博士后科学基金项目(2021M700077,2023T160348),山东省青年泰山学者项目(tsqnz20221133,tsqn202306218),山东省自然科学基金项目(ZR2024QF026,ZR2024YQ032)资助.Supported by the National Natural Science Foundation of China(62103214,62373208,62273189),the Postdoctoral Science Foundation of China(2021M700077,2023T160348),the Shandong Province's Taishan Scholars(tsqnz20221133,tsqn202306218)and the Natural Science Foundation of Shandong Province(ZR2024QF026,ZR2024YQ032). （62103214,62373208,62273189）
