[1] 梁秀霞, 庞荣荣, 郭鹭, 等. 基于AMWPSO-LSTM的多阶段间歇过程故障预测[J]. 北京化工大学学报(自然科学版), 2022, 49(2): 116-124 LIANG Xiuxia, PANG Rongrong, GUO Lu,et al. Multi-stage intermittent process fault prediction based on AMWPSO-LSTM[J]. Journal of Beijing University of Chemical Technology (Natural Science Edition), 2022, 49(2): 116-124(in Chinese)
[2] 纪成, 顾俊发, 王健红, 等. 基于长短时记忆神经网络和支持向量数据描述的间歇过程监测方法[J]. 化学工业与工程, 2022, 39(2): 32-36 JI Cheng, GU Junfa, WANG Jianhong,et al. A batch process monitoring method based on long short term memory network and support vector data description[J]. Chemical Industry and Engineering, 2022, 39(2): 32-36(in Chinese)
[3] NOMIKOS P, MACGREGOR J F. Multi-way partial least squares in monitoring batch processes[J]. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 97-108
[4] JIANG Q, YAN X, YI H,et al. Data-driven batch-end quality modeling and monitoring based on optimized sparse partial least squares[J]. IEEE Transactions on Industrial Electronics, 2020, 67(5): 4098-4107
[5] LUO L, BAO S, MAO J,et al. Quality prediction and quality-relevant monitoring with multilinear PLS for batch processes[J]. Chemometrics and Intelligent Laboratory Systems, 2016, 150: 9-22
[6] 王普, 曹彩霞, 高学金, 等. 基于扩展得分矩阵的多阶段间歇过程质量预测[J]. 高校化学工程学报, 2019, 33(3):664-671 WANG Pu, CAO Caixia, GAO Xuejin,et al. Quality prediction of multistage batch processes based on extended score matrices[J]. Journal of Chemical Engineering of Chinese Universities, 2019, 33(3): 664-671(in Chinese)
[7] WANG K, GOPALUNI R B, CHEN J,et al. Deep learning of complex batch process data and its application on quality prediction[J]. IEEE Transactions on Industrial Informatics, 2020, 16(12): 7233-7242
[8] QI J, LUO N. Using stacked auto-encoder and Bi-directional LSTM for batch process quality prediction[J]. Journal of Chemical Engineering of Japan, 2021, 54(4): 144-151
[9] 李泽龙, 杨春节, 刘文辉, 等. 基于LSTM-RNN模型的铁水硅含量预测[J]. 化工学报, 2018, 69(3): 992-997 LI Zelong, YANG Chunjie, LIU Wenhui,et al. Research on hot metal Si-content prediction based on LSTM-RNN[J]. CIESC Journal, 2018, 69(3): 992-997(in Chinese)
[10] ZOU G, YAN Z, ZHANG C,et al. Transfer learning with CNN-LSTM model for capacity prediction of lithium-ion batteries under small sample[J]. Journal of Physics: Conference Series, 2022, 2258(1): 012042
[11] MA J, ZENG X, XUE X,et al. Metro emergency passenger flow prediction on transfer learning and LSTM model[J]. Applied Sciences, 2022, 12(3): 1644
[12] 郑真, 朱峰, 马小丽, 等. 基于TL-LSTM的新能源功率短期预测[J]. 综合智慧能源, 2023(1): 41-48 ZHENG Zhen, ZHU Feng, MA Xiaoli,et al. Short-term new energy power prediction based on TL-LSTM[J]. Integrated Intelligent Energy, 2023(1): 41-48(in Chinese)
[13] CHEN W, CHEN W, LIU H,et al. A RUL prediction method of small sample equipment based on DCNN-BiLSTM and domain adaptation[J]. Mathematics, 2022, 10(7): 1022
[14] FU B, WU Z, GUO J. Remaining useful life prediction under multiple operation conditions based on domain adaptive sparse auto-encoder[C]//2020 IEEE International Conference on Prognostics and Health Management (ICPHM). Detroit, MI, USA. IEEE, 2020
[15] LI X, ZHANG W, DING Q,et al. Multi-Layer domain adaptation method for rolling bearing fault diagnosis[J]. Signal Processing, 2019, 157: 180-197
[16] DE OLIVEIRA DA COSTA P R, AKÇAY A, ZHANG Y Q,et al. Remaining useful lifetime prediction via deep domain adaptation[J]. Reliability Engineering & System Safety, 2020, 195: 106682
[17] LIU J, ZHOU Z, HE X,et al. Winter wheat yield estimation method based on NDWI and convolutional neural network[J].Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(12): 273-280
[18] ZHAO J, MAO X, CHEN L. Speech emotion recognition using deep 1D & 2D CNN LSTM networks[J]. Biomedical Signal Processing and Control, 2019, 47: 312-323
[19] 冯勇, 冯述放, 罗娜. 基于时钟触发长短期记忆的多元时序预测[J]. 华东理工大学学报(自然科学版), 2023, 49(2): 255-268 FENG Yong, FENG Shufang, LUO Na. Multivariate time series prediction based on clockwork triggered long short term memory[J]. Journal of East China University of Science and Technology, 2023, 49(2): 255-268(in Chinese)
[20] YUAN X, LI L, SHARDT Y A W,et al. Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development[J]. IEEE Transactions on Industrial Electronics, 2021, 68(5): 4404-4414
[21] DING Y, ZHU Y, FENG J,et al. Interpretable spatio-temporal attention LSTM model for flood forecasting[J]. Neurocomputing, 2020, 403: 348-359
[22] GUPTA J, PATHAK S, KUMAR G. Deep learning (CNN) and transfer learning: A review[J]. Journal of Physics: Conference Series, 2022, 2273(1): 012029
[23] 成于思, 施云涛. 基于深度学习和迁移学习的领域自适应中文分词[J]. 中文信息学报, 2019, 33(9):9-16, 23 CHENG Yusi, SHI Yuntao. Domain adaption of Chinese word segmentation based on deep learning and transfer learning[J]. Journal of Chinese Information Processing, 2019, 33(9): 9-16, 23(in Chinese)
[24] ZHOU Q, ZHOU W, WANG S,et al. Multiple adversarial networks for unsupervised domain adaptation[J]. Knowledge-Based Systems, 2021, 212: 106606
[25] 褚菲, 彭闯, 贾润达, 等. 基于多尺度核JYMKPLS迁移模型的间歇过程产品质量的在线预测方法[J]. 化工学报, 2021, 72(4):2178-2189 CHU Fei, PENG Chuang, JIA Runda,et al. Online prediction method of batch process product quality based on multi-scale kernel JYMKPLS transfer model[J]. CIESC Journal, 2021, 72(4): 2178-2189(in Chinese)
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2024, Vol. 41 
