[1] |
ZHENG W, YANG Y. Airship flight mechanics and control [M]. Beijing: Science Press, 2016 (in Chinese).
|
[2] |
LI F, YE Z Y. Aerodynamic configuration: Design of new type buoyancy-lifting airship [M]. Beijing: National Defense Industry Press, 2016 (in Chinese).
|
[3] |
ZHAO D, LIU D X, SUN K W, et al. Research status, technical difficulties and development trend of stratospheric airship [J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(1): 45-56 (in Chinese).
|
[4] |
WANG X L, SHAN X X. Aerodynamic estimation for stratosphere airship [J]. Chinese Quarterly of Mechanics, 2006, 27(2): 295-304 (in Chinese).
|
[5] |
LIU P, FU G, ZHU L, et al. Aerodynamic characteristics of airship Zhiyuan-1 [J]. Journal of Shanghai Jiaotong University (Science), 2013, 18(6): 679-687.
|
[6] |
JONES S P, DELAURIER J D. Aerodynamic estimation techniques for aerostats and airships [J]. Journal of Aircraft, 1983, 20(2): 120-126.
|
[7] |
LIU Z L. On the Calculation methods of aerodynamic force and moment in airship modeling [D]. Xiamen: Xiamen University, 2019 (in Chinese).
|
[8] |
MENG J H, LI M N, ZHANG L C, et al. Aerodynamic performance analysis of hybrid air vehicles with large Reynolds number [J]. Unmanned Systems Technology, 2020, 3(1): 38-47 (in Chinese).
|
[9] |
JIAO L C, YANG S Y, LIU F, et al. Seventy years beyond neural networks: Retrospect and prospect [J]. Chinese Journal of Computers, 2016, 39(8): 1697-1716 (in Chinese).
|
[10] |
LOPEZ-GARCIA T B, CORONADO-MENDOZA A, DOMíINGUEZ-NAVARRO J A. Artificial neural networks in microgrids: A review [J]. Engineering Applications of Artificial Intelligence, 2020, 95: 103894.
|
[11] |
TAO J, SUN G, GUO L Q, et al. Application of a PCADBN-based surrogate model to robust aerodynamic design optimization [J]. Chinese Journal of Aeronautics, 2020, 33(6): 1573-1588.
|
[12] |
CHEN H, QIAN W Q, HE L. Aerodynamic coefficient prediction of airfoils based on deep learning [J]. Acta Aerodynamica Sinica, 2018, 36(2): 294-299 (in Chinese).
|
[13] |
WANG C, WANG G D, BAI P. Machine learning method for aerodynamic modeling based on flight simulation data [J]. Acta Aerodynamica Sinica, 2019, 37(3): 488-497 (in Chinese).
|
[14] |
ZHANG Z C, GAO T Y, ZHANG L, et al. Aeroheating agent model based on radial basis function neural network [J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524167 (in Chinese).
|
[15] |
SPALART P, ALLMARAS S. A one-equation turbulence model for aerodynamic flows [C]//30th Aerospace Sciences Meeting and Exhibit. Reno, NV: AIAA, 1992.
|
[16] |
FUNK P, LUTZ T, WAGNER S. Experimental investigations on hull-fin interferences of the LOTTE airship [J]. Aerospace Science and Technology, 2003, 7(8): 603-610.
|
[17] |
POMPONI J, SCARDAPANE S, UNCINI A. Bayesian neural networks with maximum mean discrepancy regularization [J]. Neurocomputing, 2021, 453: 428-437.
|
[18] |
HEYDECKER B G, WU J. Identification of sites for road accident remedial work by Bayesian statistical methods: An example of uncertain inference [J]. Advances in Engineering Software, 2001, 32(10/11): 859-869.
|
[19] |
SUN Z, CHEN Y, LI X Y, et al. A Bayesian regularized artificial neural network for adaptive optics forecasting [J]. Optics Communications, 2017, 382: 519-527.
|
[20] |
HORNIK K, STINCHCOMBE M, WHITE H. Multilayer feedforward networks are universal approximators [J]. Neural Networks, 1989, 2(5): 359-366.
|
[21] |
CHEN M. Principles and examples of MATLAB neural network [M]. Beijing: Tsinghua University Press, 2013 (in Chinese).
|
[22] |
LIU J P, ZHAO B T, QIAN W F, et al. Modeling and prediction of particle cutoff size of cyclone separator based on BP neural network [J]. Chemical Industry and Engineering Progress, 2021, 40(2): 671-677 (in Chinese).
|