Cite

Li Wenbin. Research on the essence of industrial revolution 4.0 and its impact [D]. China University of Mining and Technology, 2019. Li Wenbin Research on the essence of industrial revolution 4.0 and its impact [D] China University of Mining and Technology 2019 Search in Google Scholar

Zhu Chun Chuan. The use of product traceability system in automated production [J]. Metallurgical Management, 2020(17):75-76. Zhu Chun Chuan The use of product traceability system in automated production [J] Metallurgical Management 20201775 76 Search in Google Scholar

Dong Yanchao. Research on key technologies of RFID-based industrial product traceability system [D]. Dalian Maritime University, 2017. Dong Yanchao Research on key technologies of RFID-based industrial product traceability system [D] Dalian Maritime University 2017 Search in Google Scholar

Xie W, Xie K. Hang A, Wan G, Qu I, Zhang Q, Tang C J. RFID reseraching: Finding a lot tag rather than only detecting its missing[J]. Journal of Network and Computer Applications, 2014, Volume 41:95-120. Xie W Xie K. Hang A Wan G Qu I Zhang Q Tang C J RFID reseraching: Finding a lot tag rather than only detecting its missing[J] Journal of Network and Computer Applications 2014, Volume 41 95120 10.1016/j.jnca.2014.01.006 Search in Google Scholar

Cheng Zhaolan, Zhang Xiaoqiang, and Liang Yue. Railway freight volume forecasting based on LSTM networks [J]. Journal of Railways, 2020, v.42; No.277(11):19-25. Cheng Zhaolan Zhang Xiaoqiang , and Liang Yue Railway freight volume forecasting based on LSTM networks [J] Journal of Railways 2020, v.42; No.277(11):1925 Search in Google Scholar

Wei X, Zhang L, Yang H Q, et al. Machine learning for pore-water pressure time-series prediction: application of recurrent neural networks [J]. Geoscience Frontiers, 2020. Wei X Zhang L Yang H Q Machine learning for pore-water pressure time-series prediction: application of recurrent neural networks [J] Geoscience Frontiers 2020 10.1016/j.gsf.2020.04.011 Search in Google Scholar

Xingjian Shi, Zhourong Chen, Hao Wang, et al. Convolutional LSTM Network: a Machine Learning Approach for Precipitation Nowcasting [J]. NIPS 2015. Xingjian Shi Zhourong Chen Hao Wang Convolutional LSTM Network: a Machine Learning Approach for Precipitation Nowcasting [J] NIPS 2015 Search in Google Scholar

Hu H, Yang Y. A combined GLQP, and DBN-DRF for face recognition in unconstrained environments[C]. 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017), 2017. Hu H Yang Y A combined GLQP, and DBN-DRF for face recognition in unconstrained environments[C] 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017) 2017 10.2991/caai-17.2017.124 Search in Google Scholar

LIN M, CHEN Q, YAN S. Network in network [EB/OL]. [2013-12-16]. https://arxiv.org/abs/1312.4400. LIN M CHEN Q YAN S Network in network [EB/OL]. [2013-12-16]. https://arxiv.org/abs/1312.4400. 10.3923/itj.2013.3759.3763 Search in Google Scholar

Goodfellow, I., Bengio, Y., Courville, A.. Deep learning (Vol.1). Cambridge: MIT press, 2016:326-366. Goodfellow I. Bengio Y. Courville A. Deep learning (Vol.1) Cambridge MIT press 2016 326 366 Search in Google Scholar

Gu J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, L., Wang, G. and Cai [J]. 2015. Recent advances in convolutional neural networks. arXiv preprint arXiv:1512.07108. Gu J. Wang Z. Kuen J. Ma L. Shahroudy A. Shuai B. Liu T. Wang X. Wang L. Wang G. and Cai [J] 2015 Recent advances in convolutional neural networks. arXiv preprint arXiv:1512.07108 Search in Google Scholar

Raghu M, Poole B, Kleinberg J, et al. On the expressive power of deep neural networks [C]//Proceedings of the 34th International Conference on Machine Learning-Volume 70. jmlr. org, 2017: 2847-2854. Raghu M Poole B Kleinberg J On the expressive power of deep neural networks [C]//Proceedings of the 34th International Conference on Machine Learning -Volume 70. jmlr. org, 2017: 2847-2854 Search in Google Scholar

Bianchini M, Scarselli F. On the complexity of neural network classifiers: a comparison between shallow and deep architectures [J]. IEEE transactions on neural networks and learning systems, 2014, 25(8): 1553-1565. Bianchini M Scarselli F On the complexity of neural network classifiers: a comparison between shallow and deep architectures [J] IEEE transactions on neural networks and learning systems 2014 258 1553 1565 10.1109/TNNLS.2013.229363725050951 Search in Google Scholar

Yang Guanci, Yang Jing, Li Shaobo, et al. Modified CNN algorithm based on Dropout and ADAM optimizer [J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2018, 46(7): 122-127. Yang Guanci Yang Jing Li Shaobo Modified CNN algorithm based on Dropout and ADAM optimizer [J] Journal of Huazhong University of Science and Technology (Natural Science Edition) 2018 467 122127 Search in Google Scholar

Chang Zihan. Electricity Price Prediction Based on Hybrid Model of Adam optimized LSTM Neural Network and Wavelet Transform [D]. Lanzhou: Lanzhou University, 2019. Chang Zihan Electricity Price Prediction Based on Hybrid Model of Adam optimized LSTM Neural Network and Wavelet Transform [D] Lanzhou Lanzhou University 2019 10.1016/j.energy.2019.07.134 Search in Google Scholar

eISSN:
2470-8038
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, other