[1] |
JIANG Y K, CHEN J H, ZHOU H C, et al. Contour error modeling and compensation of CNC machining based on deep learning and reinforcement learning[J]. The International Journal of Advanced Manufacturing Technology, 2022,118(1-2):551-570.
|
[2] |
郑捷. NLP汉语自然语言处理原理与实践[M]. 北京:电子工业出版社, 2017.
|
[3] |
李洋,董红斌. 基于CNN和BiLSTM网络特征融合的文本情感分析[J]. 计算机应用, 2018,38(11):3075-3080.
|
[4] |
ZHANG Q, CHEN X N. A multi-label classification approach for ICT fault text analysis[C]// Proceedings of the 2019 12th International Symposium on Computational Intelligence and Design (ISCID). 2019,2:241-244.
|
[5] |
苏金树,张博锋,徐昕. 基于机器学习的文本分类技术研究进展[J]. 软件学报, 2006,17(9):1848-1859.
|
[6] |
GUO Z Y, WANG J, YAN Z. Passivity and passification of memristor-based recurrent neural networks with time-varying delays[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014,25(11):2099-2109.
|
[7] |
JOULIN A, GRAVE E, BOJANOWSKI P, et al. Bag of tricks for efficient text classification[J]. arXiv preprint arXiv:1607.01759, 2016.
|
[8] |
陈文亮,朱靖波,朱慕华,等. 基于领域词典的文本特征表示[J]. 计算机研究与发展, 2005,42(12):2155-2160.
|
[9] |
HAN D, KOLLI K K, GRANSAR H, et al. Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches[J]. Journal of Cardiovascular Computed Tomography, 2020,14(2):168-176.
|
[10] |
汪海燕,黎建辉,杨风雷. 支持向量机理论及算法研究综述[J]. 计算机应用研究, 2014,31(5):1281-1286.
|
[11] |
ZHANG S C, LI X L, ZONG M, et al. Learning k for kNN classification[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2017,8(3). DOI: 10.1145/2990508.
|
[12] |
LI K H, LI C T. Locally weighted learning for naive Bayes classifier[J]. arXiv preprint arXiv:1412.6741, 2014.
|
[13] |
ZHENG Y, LIU Q, CHEN E H, et al. Exploiting multi-channels deep convolutional neural networks for multivariate time series classification[J]. Frontiers of Computer Science, 2016,10(1):96-112.
|
[14] |
陈涛,徐小力. 基于动态灰神经网络的关键设备状态趋势预测[J]. 自动化与仪表, 2016,31(6):1-4.
|
[15] |
王鑫,吴际,刘超,等. 基于LSTM循环神经网络的故障时间序列预测[J]. 北京航空航天大学学报, 2018,44(4):772-784.
|
[16] |
曹正志,叶春明. 基于并联CNN-SE-Bi-LSTM的轴承剩余使用寿命预测[J]. 计算机应用研究, 2021,38(7):2103-2107.
|
[17] |
BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473, 2014.
|
[18] |
KUMAR P S, BEHERA H S, KUMARI A, et al. Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades[J]. Computer Science Review,2020,38. DOI: 10.1016/j.cosrev.2020.
|
|
100288.
|
[19] |
ALOYSIUS N, GEETHA M. A review on deep convolutional neural networks[C]// Proceedings of the 2017 International Conference on Communication and Signal Processing (ICCSP). 2017:588-592.
|
[20] |
KALCHBRENNER N, BLUNSOM P. Recurrent convolutional neural networks for discourse compositionality[J]. arXiv preprint arXiv:1306.3584, 2013.
|
[21] |
ZHANG T, ZHANG D G, QIU J N, et al. A kind of novel method of power allocation with limited cross-tier interference for CRN[J]. IEEE Access, 2019,7:82571-82583.
|
[22] |
YAO G L, LEI T, ZHONG J D. A review of convolutional-neural-network-based action recognition[J]. Pattern Recognition Letters, 2019,118:14-22.
|
[23] |
王丽亚,刘昌辉,蔡敦波,等. 基于CNN-BiLSTM网络引入注意力模型的文本情感分析[J]. 武汉工程大学学报, 2019,41(4):386-391.
|
[24] |
陈云飞. 数据驱动的机床故障诊断知识图谱构建与预测维护研究[D]. 西安:长安大学, 2021.
|
[25] |
祖月芳,凌海风,吕永顺. 基于NLP技术的装备故障文本匹配算法研究[J]. 兵器装备工程学报, 2021,42(11):204-208.
|
[26] |
杨嘉明. 基于LSTM-BP神经网络的列控车载设备故障诊断方法[D]. 北京:北京交通大学, 2018.
|
[27] |
林海香,陆人杰,卢冉,等. 基于文本挖掘的铁路信号设备故障自动分类方法[J]. 云南大学学报(自然科学版), 2022,44(2):281-289.
|
[28] |
王轩,顾峰,闵帆,等. 基于代表的交叉验证分类[J]. 重庆邮电大学学报(自然科学版), 2021,33(5):826-833.
|
[29] |
魏东,龚庆武,来文青,等. 基于卷积神经网络的输电线路区内外故障判断及故障选相方法研究[J]. 中国电机工程学报, 2016,36(S1):21-28.
|
[30] |
解天舒. 基于卷积神经网络的Dropout方法研究[D]. 成都:电子科技大学, 2021.
|
[31] |
刘会永,张松,李剑峰,等. 采用改进CNN-BiLSTM模型的刀具磨损状态监测[J/OL]. 中国机械工程:1-10(2021-10-19)[2022-04-13]. https://kns.cnki.net/kcms/detail/42.1294.th.20211008.1648.002.html.
|
[32] |
王建辉,冉金鑫,沈莹莹,等. 基于GA-Adam优化算法的BP神经网络农业灌水量预测模型[J]. 中国农村水利水电, 2022(4):138-143.
|
[33] |
杨柯,范世东. 基于长短期记忆网络时序数据趋势预测及应用[J]. 推进技术, 2021,42(3):675-682.
|
[34] |
WANG Y J, YANG K, LI H G. Industrial time-series modeling via adapted receptive field temporal convolution networks integrating regularly updated multi-region operations based on PCA[J]. Chemical Engineering Science, 2020,228. DOI: 10.1016/j.ces.2020.115956.
|
[35] |
冷佳,刘镇,张笑非,等. 多特征融合CNN网络的旋转机械故障诊断研究[J]. 软件导刊, 2021,20(9):44-50.
|
[36] |
ZEILER M D. ADADELTA: An adaptive learning rate method[J]. arXiv preprint arXiv:1212.5701, 2012.
|
[37] |
ZHANG W L, WANG N, CHEN K Z, et al. A pruning method for deep convolutional network based on heat map generation metrics[J]. Sensors,2022,22(5).DOI: 10.3390/
|
|
s22052022.
|
[38] |
戴稳,张超勇,孟磊磊,等. 采用深度学习的铣刀磨损状态预测模型[J]. 中国机械工程, 2020,31(17):2071-2078.
|