Gated Broad Learning System Based on Deep Cascaded for Soft Sensor Modeling of Industrial Process

doi:10.1109/TIM.2022.3170967

	Gated Broad Learning System Based on Deep Cascaded for Soft Sensor Modeling of Industrial Process
	Mou, Miao; Zhao, Xiaoqiang
刊名	IEEE Transactions on Instrumentation and Measurement
	2022
卷号	71
关键词	Data mining Deep learning Extraction Broad learning system Deep learning Feature nodes Features extraction Gated neuron Industrial processs Loss measurement Soft sensors
ISSN号	0018-9456
DOI	10.1109/TIM.2022.3170967
英文摘要	With the advancement of computer and sensor technology, soft sensors have been more and more extensively used in industrial processes. Soft sensors based on deep learning often need to redesign the structure and retrain the model when the prediction results are poor, which consumes a lot of time. Therefore, a deep cascade-gated broad learning system with fast update capability is proposed for industrial process soft sensor modeling. Being inspired by deep learning, the hidden layer features extracted by the autoencoder (AE) are used in the feature nodes of the broad learning system (BLS) to obtain the deep-BLS (D-BLS), which can circumvent the problem of insufficient feature extraction caused by stochastically generated weights in the feature nodes of BLS. On this basis, each feature node is integrated and sent to the enhancement nodes through the gated neurons. The enhancement nodes are cascaded to construct the deep cascaded-gated BLS (DC-GBLS), which can improve the prediction effect of the model while enhancing the utilization rate of the hidden layer features. Finally, a fast update method is developed for the model when the accuracy is insufficient. The validity and superiority of proposed model are demonstrated by two industrial processes. © 1963-2012 IEEE.
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/158456]
专题	电气工程与信息工程学院
作者单位	Lanzhou University of Technology, College of Electrical and Information Engineering, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Mou, Miao,Zhao, Xiaoqiang. Gated Broad Learning System Based on Deep Cascaded for Soft Sensor Modeling of Industrial Process[J]. IEEE Transactions on Instrumentation and Measurement,2022,71.
APA	Mou, Miao,&Zhao, Xiaoqiang.(2022).Gated Broad Learning System Based on Deep Cascaded for Soft Sensor Modeling of Industrial Process.IEEE Transactions on Instrumentation and Measurement,71.
MLA	Mou, Miao,et al."Gated Broad Learning System Based on Deep Cascaded for Soft Sensor Modeling of Industrial Process".IEEE Transactions on Instrumentation and Measurement 71(2022).