参考文献(References):
[1] 杨燕辉.飞机引气系统的建模与故障机理研究[D].天津:中国民航大学,2013.
[2] 石旭东,蒋贵嘉,张宇,等.基于联合仿真的飞机空调系统故障影响[J].航空学报,2020,41(8):295-303.
[3] 钞迪.飞机温度控制系统故障模拟与影响分析[D].天津:中国民航大学,2019.
[4] SUN J Z,WANG F Y,NING S G.Aircraft air conditioning system health state estimation and prediction for predictive maintenance[J].Chinese Journal of Aeronautics,2020,33(3):947-955.
[5] 耿振翔,王利辉,刘慎洋,等.基于TRNSYS的飞机空调保障装备送风特性仿真研究[J].数学的实践与认识,2019,49(9):117-123.
[6] 曹国刚,李梦雪,陈颖,等.改进支持向量机分类方法及其在原发性肝癌筛查中的应用[J].应用科学学报,2021,39(3):481-494.
[7] 赵楠,李洁.基于LSTM-SVM的隧道围岩位移预测[J].公路,2021,66(6):404-407.
[8] 宫毓斌,滕欢.基于GOA-SVM的短期负荷预测[J].电测与仪表,2019,56(14):12-16.
[9] 亓晓燕,刘恒杰,侯秋华,等.融合LSTM和SVM的钢铁企业电力负荷短期预测[J].山东大学学报(工学版),2021,51(4):91-98.
[10] VELáSQUEZ R M A.Support vector machine and tree models for oil and Kraft degradation in power transformers[J].Engineering Failure Analysis,2021,127:105488.
[11] AHMED N,RABBI S,RAHMAN T,et al.Traffic sign detection and recognition model using support vector machine and histogram of oriented gradient[J].International Journal of Information Technology and Computer Science,2021,13(3):61-73.
[12] 马婷婷,杨志霞,叶俊佑.鲁棒双参数化间隔支持向量机[J].计算机工程与应用,2022,58(9):74-82
[13] LAMESKI P,ZDRAVEVSKI E,MINGOV R,et al.SVM parameter tuning with grid search and its impact on reduction of model over-fitting[C]// Rough Sets,Fuzzy Sets,Data Mining,and Granular Computing:Vol 9437.Cham:Springer,2015:464-474.
[14] DO T N,POULET F.Parallel learning of local SVM algorithms for classifying large datasets[C]//Transactions on Large-Scale Data- and Knowledge-Centered Systems:Volume 10140.Berlin:Springer,2017:67-93.
[15] SAREMI S,MIRJALILI S,LEWIS A.Grasshopper optimisation algorithm:theory and application[J].Advances in Engineering Software,2017,105:30-47.
[16] WANG Z Y,YAO L G,CAI Y W.Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine[J].Measurement,2020,156:107574.
[17] GU P,FENG Y Z,ZHU L,et al.Unified classification of bacterial colonies on different agar media based on hyperspectral imaging and machine learning[J].Molecules,2020,25(8):1797.
[18] SAYED G I,SOLIMAN M,HASSANIEN A E.Modified optimal foraging algorithm for parameters optimization of support vector machine[C]//The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018),February 22-24,2018,Cairo,Egypt:Vol 723.Cham:Springer,2018:23-32.
[19] WU K P,WANG S D.Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space [J].Pattern Recognition,2009,42(5):710-717.
[20] QIN H S,WEI Y,ZENG S H.Parameter optimization for SVM classification based on NGA[C]//Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012,October 26-28,2012,Chongqing,China:Vol 216.London:Springer,2013:579-586.
[21] 周伟,谢利娟,杨晗,等.基于高光谱的三江源区土壤有机质含量反演[J].土壤通报,2021,52(3):564-574.
[22] 张育凡.基于蚱蜢优化和最小二乘支持向量机的电力负荷预测研究[D].兰州:兰州大学,2018.
[23] 王生生,张伟,董如意,等.改进蚱蜢算法在电动汽车充换电站调度中的应用[J].东北大学学报(自然科学版),2020,41(2):170-175.
[24] 崔东文,郭荣.基于GOA-PP模型的区域水资源红黄绿分区管理识别[J].华北水利水电大学学报(自然科学版),2018,39(1):68-76.
[25] 吕赵明,张颖江.基于改进GOA-SVM算法的异常流量识别[J].湖南科技大学学报(自然科学版),2019,34(4):90-96.