[9] XU Q F, ZHOU G H, ZHANG C, et al. Digital twindriven intelligent maintenance decision-making systemand key-enabling technologies for nuclear power equipment[ J] . Digital Twin, 2022, 2: 14.
[10] 姜伟强, 孔令海, 唐武成, 等. 水洗对燃气透平效率影响浅析[ J] . 中国石油石化, 2017(4) : 12-14.
JIANG W Q, KONG L H, TANG W C, et al. Analysison the influence of water washing on gas turbine efficiency[ J] . China Petrochem, 2017(4) : 12-14.
[11] 韩朝兵, 朱泓逻, 黄伟栋, 等. 压气机离线水洗后燃气轮机性能衰退分析研究[ J] . 动力工程学报, 2019,39(8) : 626-633.
HAN C B, ZHU H L, HUANG W D, et al. Performancedegradation analysis of a gas turbine after offline waterwashing of the compressor[ J] . Journal of Chinese Societyof Power Engineering, 2019, 39(8) : 626-633.
[12] 陈家伦, 卞韶帅, 黄新. 基于 BP 神经网络的 9F 燃气轮机压气机离线水洗周期优化 [ J] . 燃气轮机技术,2020, 33(1) : 47-53.
CHEN J L, BIAN S S, HUANG X. Optimization of offline washing cycle of 9F gas turbine compressor based onBP neural network[ J] . Gas Turbine Technology, 2020,33(1) : 47-53.
[13] RAO P N S, ACHUTHA NAIKAN V N. An optimal maintenance policy for compressor of a gas turbine powerplant[ J] . Journal of Engineering for Gas Turbines andPower, 2008, 130(2) : 1.
[14] 汪祖民, 王冬昊, 梁霞, 等. 基于 DBSCAN_GAN_XGBOOST 的网络入侵检测方法[ J] . 郑州大学学报( 工学版) , 2022, 43(3) : 44-51.
WANG Z M, WANG D H, LIANG X, et al. Network intrusion detection method based on DBSCAN _GAN _XGBOOST[ J] . Journal of Zhengzhou University ( Engineering Science) , 2022, 43(3) : 44-51.
[15] CHOI H S, KIM S, OH J E, et al. XGBoost-based instantaneous drowsiness detection framework using multitaper spectral information of electroencephalography[C]∥Proceedings of the 2018 ACM International Conference onBioinformatics, Computational Biology, and Health Informatics. New York: ACM, 2018: 111-121.
[16] CHEN T Q, GUESTRIN C. Xgboost: a scalable treeboosting system [ C ] ∥ Proceedings of the 22nd ACMSIGKDD International Conference on Knowledge Discoveryand Data Mining. New York: ACM, 2016: 785-794.
[17] HENDERI H. Comparison of min-max normalization andz-score normalization in the k-nearest neighbor ( kNN) algorithm to test the accuracy of types of breast cancer[ J] .IJIIS: International Journal of Informatics and InformationSystems, 2021, 4(1) : 13-20.
[18] ISLAM S N, SHOLAHUDDIN A, ABDULLAH A S. Extreme gradient boosting ( XGBoost ) method in makingforecasting application and analysis of USD exchange ratesagainst rupiah [ J] . Journal of Physics: Conference Series, 2021, 1722(1) : 012016.
[19] TOPIC A, RUSSO M. Emotion recognition based on EEGfeature maps through deep learning network [ J] . Engineering Science and Technology, an International Journal, 2021, 24(6) : 1442-1454.
[20] OGUNLEYE A, WANG Q G. XGBoost model for chronickidney disease diagnosis[ J] . IEEE / ACM Transactions on Computational Biology and Bioinformatics, 2020, 17(6) :2131-2140.
[21] CHAI T, DRAXLER R R. Root mean square error(RMSE) or mean absolute error ( MAE) ? Arguments against avoiding RMSE in the literature[ J] . GeoscientificModel Development, 2014, 7(3) : 1247-1250.
[22] SHAHID F, ZAMEER A, MUNEEB M. Predictions forCOVID-19 with deep learning models of LSTM, GRU andBi-LSTM [ J ] . Chaos, Solitons & Fractals, 2020,140: 110212.
[23] DE MYTTENAERE A, GOLDEN B, LE GRAND B, etal. Mean Absolute Percentage Error for regression models[ J] . Neurocomputing, 2016, 192: 38-48.
[24] 陈创庭. 大型电站燃气轮机的水洗经济周期研究[ J] .广东电力, 2009, 22(7) : 18-23.
CHEN C T. Research on economic wash period of heavyduty power gas turbine [ J] . Guangdong Electric Power,2009, 22(7) : 18-23.
[25] 程元, 陈坚红, 盛德仁, 等. 联合循环发电机组燃气轮机水洗策略优化模型研究[ J] . 中国电机工程学报,2013, 33(26) : 95-100.
CHENG Y, CHEN J H, SHENG D R, et al. Researchon washing strategy optimization model of combined cyclegas turbines [ J ] . Proceedings of the CSEE, 2013, 33(26) : 95-100.
[26] 李爱民, 王海隆, 许有成. 优化随机森林算法的城市湖泊 DOC 质量浓度遥感反演[ J] . 郑州大学学报( 工学版) , 2022, 43(6) : 90-96.
LI A M, WANG H L, XU Y C. Remote sensing retrievalof urban lake DOC concentration based on optimized random forest algorithm[ J] . Journal of Zhengzhou University(Engineering Science) , 2022, 43(6) : 90-96.