锻压技术

基于改进ELM-AE冷轧轧制力预测

英文标题：Prediction on cold rolling force based on improved ELM-AE

作者：张志强尚猛张宇

分类号：TP183

出版年,卷(期):页码：2019,44(12):192-197

摘要：

在研究深度神经网络预测轧制力的基础上，针对极限学习机-自编码器的回归问题，提出一种自增删网络结构优化算法。利用自编码器进行原始数据的特征提取，为模型提供有效的高阶特征。极限学习机的学习速度快且泛化能力强，监督阶段时使用极限学习机回归轧制力，并使用隐层节点增删策略调节极限学习机的网络结构，解决了极限学习机-自编码器的结构设计问题。该方法用于轧制力回归，采用深层网络结合大量数据保证模型的回归精度的同时，实现了轧制数据的特征提取和网络结构的自增删。结果显示，该深层结构自增删网络具有很好的模型收敛和参数回归能力，在训练速度与精度方面均优于弹性RBF和稀疏自编码器神经网络算法。

On the basis of studying the prediction of rolling force by deep neural network, a self-addition and deletion network structure optimization algorithm was proposed for the regression problem of extreme learning machine-autoencoder. Then, the features of the original data were extracted by the autoencoder to provide effective high-order features for the model. However, the learning speed of the extreme learning machine was fast and the generalization ability was strong, and the rolling force was regressed by the extreme learning machine in the supervision stage. Therefore, the network structure of the extreme learning machine was adjusted by adding and deleting the hidden layer nodes to solve the structural design issues of the extreme learning machine-autoencoder. Furthermore, the method was applied to rolling force regression, and the regression accuracy of the model was ensured by the deep network combining with a large amount of data. At the same time, the feature extraction of the rolling data and the self-addition and deletion of the network structure were realized. The result proves that the self-addition and deletion network of the deep structure has good model convergence and parameter regression ability, and it is superior to flexible RBF and sparse autoencoder neural network algorithm in training speed and accuracy.

基金项目：

国家留学基金资助项目(201708410230)

张志强(1986-)，男，硕士，初级讲师 E-mail：zhf_2008@163.com

参考文献：

［1］Deng C, Han Y, Zhao B. High-performance visual tracking with extreme learning machine framework
［J］. IEEE Transactions on Cybernetics, 2019,26(3): 1-12.

［2］韩红桂, 乔俊飞, 薄迎春. 基于信息强度的RBF神经网络结构设计研究
［J］. 自动化学报, 2012,38(7): 1083-1090.

Han H G, Qiao J F, Bo Y C. On structure design for RBF neural network based on information strength
［J］. Acta Automatica Sinica,2012, 38(7): 1083-1090.

［3］Zhao Z, Yang J, Che H, et al. Application of artificial bee colony algorithm to select architecture of a optimal neural network for the prediction of rolling force in hot strip rolling process
［J］. Journal of Chemical and Pharmaceutical Research, 2013, 5(9): 563-570.

［4］Yan W, Tang D, Lin Y. A data-driven soft sensor modeling method based on deep learning and its application
［J］. IEEE Transactions on Industrial Electronics, 2017, 64(5): 4237-4245.

［5］Yao L, Ge Z. Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application
［J］. IEEE Transactions on Industrial Electronics, 2017, 65(2): 1490-1498.

［6］Vincent P, Larochelle H, Lajoie I, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
［J］. Journal of Machine Learning Research, 2010, 11(12): 3371-3408.

［7］Zhang H, Zhang S, Yin Y. Online sequential ELM algorithm with forgetting factor for real applications
［J］. Neurocomputing, 2017, 261: 144-152.

［8］Lekamalage C K L, Song K, Huang G, et al. Multi layer multi objective extreme learning machine
［A］.Dong F.2017 IEEE International Conference on Image Processing
［C］.Beijing:IEEE,2017.

［9］Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications
［J］. Neurocomputing, 2006, 70(1-3): 489-501.

［10］何群, 王红, 江国乾, 等. 基于相关主成分分析和极限学习机的风电机组主轴承状态监测研究
［J］. 计量学报, 2018, 39(1): 89-93.

He Q, Wang H, Jiang G Q, et al. Research of wind turbine main bearing condition monitoring based on correlation PCA and ELM
［J］. Acta Metrologica Sinica, 2018, 39(1): 89-93.

［11］Toh K. Deterministic neural classification
［J］. Neural Computation, 2008, 20(6): 1565-1595.

［12］Lu C B, Mei Y. An imputation method for missing data based on an extreme learning machine auto-encoder
［J］. IEEE Access, 2018, 6: 52930-52935.

［13］Gopakumar V, Tiwari S, Rahman I. A deep learning based data driven soft sensor for bioprocesses
［J］. Biochemical Engineering Journal, 2018, 136: 28-39.

［14］Su X, Zhang S, Yin Y, et al. Prediction of hot metal silicon content for blast furnace based on multi-layer online sequential extreme learning machine
［A］. Chen X.The 37th Chinese Control Conference
［C］.Wuhan:IEEE,2018.

［15］魏立新,张宇,孙浩, 等. 基于改进OS-ELM的冷连轧在线轧制力预报
［J］.计量学报,2019,40(1):111-116.

Wei L X, Zhang Y, Sun H, et al. Online cold rolling prediction based on improved OS-ELM
［J］. Acta Metrologica Sinica, 2019,40(1):111-116.

［16］王军生, 赵启林, 矫志杰, 等. 冷连轧过程控制变形抗力模型的自适应学习
［J］. 东北大学学报, 2004, 25(10): 973-976.

Wang J S, Zhao Q L,Jiao Z J, et al. Adaptive learning of the model of deformation resistance model for tandem cold rolling process control
［J］. Journal of Northeastern University, 2004, 25(10): 973-976.

服务与反馈：

【本网站尚未开通全文下载服务】【加入收藏】