Identify some aerodynamic parameters of a airplane using the spiking neural network


  • Nguyen Duc Thanh Academy of Military Science and Technology, 17 Hoang Sam, Hanoi, Vietnam
  • Le Tran Thang Academy of Military Science and Technology, 17 Hoang Sam, Hanoi, Vietnam
  • Vuong Anh Trung Air Defence - Air Force Academy, Kim son, Trung Son Tram, Son Tay, Hanoi, Vietnam
  • Nguyen Quang Vinh Department of Control System, Bauman Moscow state Technical University, ul. Baumanskaya 2-ya, 5/1, Moscow



aerodynamic identification, nonlinear model, flying vehicle.


The main objective of this study is to propose a method for identifying aerodynamic coefficient derivatives of aircraft attitude channel using spiking neural network (SNN) and Gauss-Newton algorithm based on data obtained from actual flights. Out of these, the SNN multi-layer network was trained by Normalized Spiking Error Back Propagation, in which, in the forward propagation period, the time of output spikes is calculating by solving quadratic equations instead of detection by traditional methods. The phase of propagation of errors backward uses the step-by-step calculation instead of the conventional gradient calculation method. SNN in combination with Gauss-Newton iterative calculation algorithm proposed in this study enables the identification of aerodynamic coefficient derivatives in a nonlinear model for aerodynamic parameters with higher accuracy and faster calculation time. The identification results are compared with the results when using the Radial Basis Function (RBF) network to prove the algorithm efficiency.


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Albisser M., Berner C., Dobre S., Thomassin M., Garnier H., 2014. Aerodynamic coefficients identification procedure of a finned projectile using magnetometers and videos free flight data. In 28th ISB International Symposium on Ballistics, Atlanta, Georgia.

Audoly S., Bellu G., D’Angio L., Saccomani M., Cobelli C., 2001. Global identifiability of nonlinear models of biological systems. IEEE Transactions on Biomedical Engineering, 48(1), 55–65.

Bohte S.M., Kok J.N., La Poutre H., 2002. Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing, 48, 17–37.

Cook M.V., 2012. Flight dynamics principles: a linear systems approach to aircraft stability and control. Butterworth-Heinemann.

Filip popular, Andrzej Kasiński, 2011. Introduction to spiking neural networks: information processing, learning and applications, Institute of Control and Information Engineering, Poznan University of Technology, Poznan, Poland.

Fujimori A., Ljung L., 2006. Model identification of linear parameter varying aircraft systems. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 220(4), 337–346.

Ha L.H., Vinh N.Q., Cuong N.T., 2020. Dynamics of Self-guided Rocket Control with the optimal Angle Coordinate System Combined with Measuring Target Parameters for Frequency Modulated Continuous Wave Radar. Intelligent Computing in Engineering. Advances in Intelligent Systems and Computing, 1125, 951–962.

Klein V., Morelli E,. 2006. Aircraft system identification: Theory and Practice. Chapter Experiment Design, A. I. A. A., 1801 Alexander Bell Drive, Reston, VA, 289–329.

Ljung L., Glad T., 1994. On global identifiability for arbitrary model parametrizations. Automatica, 30(2), 265–276.

McKennoch S., Liu D., Bushnell L.G., 2006. Fast Modifications of the SpikeProp Algorithm. IEEE International Joint Conference on Neural Networks, IEEE, 48, 3970–397.

Nguyen Duc Thanh, et al., 2019. Aerodynamic coefficient identification of of airplane’s attitude channel by the output error method, Journal of Military Science and Technology, 6, 28–36.

Popular F. et al., 2010. Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting, Neural Computation, 22, 467–510.

Rahib H. Abiyev, Okyay Kaynak, Yesim On, 2012. Spiking Neural Networks for Identification and Control of Dynamic Plants, IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

Saccomani M.P., Audoly S., D’Angio L., 2003. Parameter identifiability of nonlinear systems: the role of initial conditions. Automatica, 39, 619–632.

Semenov A.D., Volkov A.V., Schipakina N.I , 2019. Parametric Identification of Nonlinear Systems by Aggregation of Static and Dynamic Neural Networks. International Multi-Conference on Industrial Engineering and Modern Technologies, 76–82.

Sjoberg J., Zhang Q., Ljung L., Beneviste A., Delyon B., Glorennec P., Hjalmarsson H., Juditsky A., 1995. Nonlinear black-box modelling in system identification: a unified overview. Automatica, 31, 1691–1724.

Tsibizova T.Y., 2016. Identification methods for nonlinear control systems, journal of computer technology, automatic control, radio electronics, 6, 11–17.

Verdult V., Lovera M., Verhaegen M., 2004. Identification of linear parameter-varying state-space models with application to helicopter rotor dynamics. Taylor & Francis, International Journal of Control, 77(13), 1149–1159.

Vinh N.Q., Phuong Anh P.T., Vu N., Lai P.T., 2019. Sliding Mode Based Lateral Control of Unmanned Aerial Vehicles. Procedia Computer Science, 150, 78–87.

Wade J.J., McDaid L.J., Santos J., Sayers H.M., 2010. A spiking neural network training algorithm for classification problems. IEEE Transactions on Neural Networks, 21, 1817–1830.

Walter E., Pronzato L., 1997. Identification of parametric models from experimental data. Springer-Verlag.

Xiurui Xie, Hong Qu, Guisong Liu, Malu Zhang, Jürgen Kurths, 2016. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks, Plos one.


How to Cite

Thanh, N. D., Thang, L. T., Trung, V. A., & Vinh, N. Q. (2020). Identify some aerodynamic parameters of a airplane using the spiking neural network. Vietnam Journal of Earth Sciences, 42(3), 276–287.