检索结果-南通市图书馆

Online Dynamic Semi-supervised broad learning system for Real-time Fault Diagnosis with Variable Working Conditions

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Online Dynamic Semi-supervised Broad Learning System for Rea...

broad learning system based on the quantized minimum error entropy criterion

第35届中国过程控制会议

作者： Zeyi Liu Pengyu Han Wei Li Shijin Chen Xiao He MCC5 Group Shanghai Co.LTD

The multi-mode fault diagnosis method under time-varying working conditions presents a significant challenge in process control. Production processes often exhibit multiple steady conditions due to variations in external environmental conditions, changes in production schemes, and fluctuations in raw material properties. The influence of operating conditions on monitoring data features may outweigh that of faults, thus significantly impacting fault diagnosis effectiveness. Effectively handling transitional conditions in fault diagnosis necessitates updating models with online monitoring data due to practical limitations in obtaining sufficient historical data. In this paper, we propose an online dynamic semi-supervised broad learning system for real-time fault diagnosis under variable working conditions. A dynamic representation center-based anchor set construction principle is designed to adapt to the evolving distribution of monitoring data over time. We also derive a model updating rule to regulate the evolutionary direction of online updates for the diagnostic model with dynamic manifold regularization term. Realistic gearbox fault diagnosis experiments under various working conditions demonstrate its effectiveness in mitigating adverse effects of transitional conditions on diagnostic performance.

关键词： Multi-mode fault diagnosis broad learning system transitional conditions online semi-supervised learning

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2022年第12期65卷 204-219页

作者： Simin ZHANG Zhulin LIU C.L.Philip CHEN School of Computer Science and Engineering South China University of Technology Pazhou Lab

The broad learning system(BLS) based on the minimum mean square error(MMSE) criterion can achieve outstanding performance without spending too much time in various machine learning ***, when data are polluted by non-Gaussian noise, the stability of BLS may be destroyed because the MMSE criterion is sensitive to outliers. Different from the MMSE criterion, the minimum error entropy(MEE) criterion utilizes the kernel function to capture high-dimensional information and decrease the negative influence of outliers, which will make BLS more discriminative and robust. Nevertheless, the computational complexity of MEE is high due to a double summation of the data size. To solve these issues, this paper proposes a new robust BLS variant based on the quantized minimum error entropy(QMEE) criterion, in which a quantization operation is used to reduce the computational complexity of MEE. The proposed model BLS-QMEE is optimized by the fixed-point iterative method, and a sufficient condition for its convergence is provided. Compared with the standard BLS and other existing robust variants of BLS, BLS-QMEE performs more satisfactorily without consuming too much time. The desirable performance of BLS-QMEE is verified by various experiments on function approximation, several public datasets, and a practical application.

关键词： broad learning system quantized minimum error entropy robustness minimum error entropy convergence

broad learning system with Preprocessing to Recover the Scattering Obstacles with Far-Field Data

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Advances in Applied Mathematics and Mechanics 2023年第4期15卷 984-1000页

作者： Weishi Yin Hongyu Qi Pinchao Meng Changchun University of Science and Technology ChangchunJilin 30000China

Based on broad learning system with preprocessing,the impenetrable obstacles were ***,the far-field data were preprocessed by Random Forest,and the shapes of the obstacles were classified by dividing the far-field data into different ***,the broad learning system was employed for reconstructing the unknown *** far-field data of the scatterer were regarded as the input nodes of mapped features in the network,and all the mapped features were connected with the enhancement nodes of random weights to the output ***,the coefficient of the output can be obtained by the *** method for the recovery of the scattering obstacles is named ***,numerical experiments revealed that the proposed method is effective,and that the training speed was significantly improved,compared with the deep learning method.

关键词： Inverse scattering problem broad learning system machine learning random forest

Discriminative graph regularized broad learning system for image recognition

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2018年第11期61卷 179-192页

作者： Junwei JIN Zhulin LIU C.L.Philip CHEN Faculty of Science and Technology University of Macau Dalian Maritime University State Key Laboratory of Management and Control for Complex systems Institute of AutomationChinese Academy of Sciences

broad learning system(BLS) has been proposed as an alternative method of deep learning. The architecture of BLS is that the input is randomly mapped into series of feature spaces which form the feature nodes, and the output of the feature nodes are expanded broadly to form the enhancement nodes, and then the output weights of the network can be determined analytically. The most advantage of BLS is that it can be learned incrementally without a retraining process when there comes new input data or neural nodes. It has been proven that BLS can overcome the inadequacies caused by training a large number of parameters in gradient-based deep learning algorithms. In this paper, a novel variant graph regularized broad learning system(GBLS) is proposed. Taking account of the locally invariant property of data, which means the similar images may share similar properties, the manifold learning is incorporated into the objective function of the standard BLS. In GBLS, the output weights are constrained to learn more discriminative information,and the classification ability can be further enhanced. Several experiments are carried out to verify that our proposed GBLS model can outperform the standard BLS. What is more, the GBLS also performs better compared with other state-of-the-art image recognition methods in several image databases.

关键词： broad learning system deep learning graph regularization image recognition feature extraction incremental learning

Target tracking algorithm based on a broad learning system

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2022年第5期65卷 261-263页

作者： Dan ZHANG Tieshan LI C.L.Philip CHEN He YANG Navigation College Dalian Maritime University Innovation and Entrepreneurship Education College Dalian Minzu University School of Automation Engineering University of Electronic Science and Technology of China Computer Science and Engineering College South China University of Technology

Target tracking is one of the most important research topics in the field of computer vision. It has been widely applied in aerospace, automatic monitoring, navigation, human-computer interaction, and artificial intel... 详细信息

关键词： Target tracking broad learning system Selective search algorithm Multi-cue tracking Tracking adaptability

Target tracking method of Siamese networks based on the broad learning system

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2023年第3期8卷 1043-1057页

作者： Dan Zhang C.L.Philip Chen Tieshan Li Yi Zuo Nguyen Quang Duy Navigation College Dalian Maritime UniversityDalianChina Innovation and Entrepreneurship Education College Dalian Minzu UniversityDalianChina Computer Science and Engineering College South China University of TechnologyGuangzhouChina Department of Computer and Information Science Faculty of Science and TechnologyUniversity of MacaoMacaoChina School of Automation Engineering University of Electronic Science and Technology of ChinaChengduChina Faculty of Navigation Vietnam Maritime UniversityHaiphongVietnam

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other ***,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time *** based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time ***,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during ***,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time *** broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning *** at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is *** method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐*** broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the *** results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

关键词： broad learning system siamese network target tracking

Identifying Lysine Succinylation Sites in Proteins by broad learning system and Optimizing Imbalanced Training Dataset via Randomly Labeling Samples

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Wuhan University Journal of Natural Sciences 2021年第1期26卷 81-88页

作者： JIA Jianhua SHEN Yanxia QIU Wangren Computer Department Jingdezhen Ceramic UniversityJingdezhen 333403JiangxiChina

As one important type of post-translational modifications(PTMs),protein lysine succinylation regulates many important biological *** is also closely involved with some major diseases in the aspects of Cardiometabolic,liver metabolic,nervous system and so ***,it is imperative to predict the succinylation sites in proteins for both basic research and drug *** this paper,a novel predictor called i Succ Lys-BLS was proposed by not only introducing a new machine learning algorithm—broad learning system,but also optimizing the imbalanced data by randomly labeling *** cross-validation and independent test indicate that the success rate of i Succ Lys-BLS for positive samples is overwhelmingly higher than its counterparts.

关键词： lysine succinylation broad learning system randomly labeling imbalanced data

维普期刊数据库

Port Throughput Forecasting Based on broad learning system with Considering Influencing Factors

在线全文

学校读者我要写书评

暂无评论

Port Throughput Forecasting Based on Broad Learning System w...

BLS-identification:A device fingerprint classification mechanism based on broad learning for Internet of Things

第32届中国控制与决策会议

作者： Yiying Li Tieshan Li Yi Zuo Navigation College Dalian Maritime University

With the development of the shipping industry,port throughput has increased significantly in recent *** ports have become increasingly *** fluctuation of container throughput in ports is affected by many *** effective and accurate forecasting of port throughput provides a scientific reference for the development of the *** on the port throughput data of Lianyun Port in China from the first quarter of 2005 to the fourth quarter of 2016,this paper uses univariate linear regression,multiple linear regression,and broad learning system to forecast the container throughput of the port in each quarter of 2017 and *** the forecasting results with the actual *** experimental results show that the broad learning system can consider two economic influncing factors about the throughput at the same time and forecast port throughput accurately and ***,broad learning system is more suitable for the forecasting of port throughput than other methods.

关键词： Forecasting Linear Regression Model Port throughput broad learning system

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Digital Communications and Networks 2024年第3期10卷 728-739页

作者： Yu Zhang Bei Gong Qian Wang Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology Zhengzhou Normal UniversityHenan450044China

The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security *** a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access ***,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or *** address this problem,a device fingerprinting mechanism based on a broad learning system(BLS)is proposed in this *** mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint *** complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using *** experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.

关键词： Device fingerprint Traffic analysis Class imbalance broad learning system Access authentication